Distributed Dynamic Channel Assignment for the Wireless ... - CiteSeerX

Distributed Dynamic Channel Assignment for the Wireless Environment

This thesis is submitted for the degree of Doctor of Philosophy (D.Phil.)

David Grace Communications Research Group Department of Electronics University of York December 16, 1998

Abstract This thesis examines Distributed Dynamic Channel Assignment (DDCA) for the wireless environment, mostly using point-to-point communications architectures, often used by the military. Performance is judged in terms of blocking and dropped call probability against traffic load; particular attention is paid to call dropping reduction. DDCA channel selection strategies are examined, using an algorithm called Unsupervised Frequency Division Multiple Access (UFDMA). It is shown that highest capacity is achieved by using the Least Interfered Channel. A novel pictorial model is developed which uses the concept of Exclusion Areas, which illustrates that call dropping in DDCA is caused by nodes being activated on the same channel in a vulnerable region. Performance of point-to-point schemes is compared against ‘All-Knowing’ Dynamic Channel Assignment (DCA) algorithms which have all information available centrally on which to make a choice. These are found to have significantly higher capacity in shadowed environments than the most basic UFDMA schemes. For the first time the similarity between packet access schemes (Multi-channel CSMA) and DDCA is highlighted. If channels are sequentially scanned, capacity is shown to limited by the number of channels to be scanned and the channel scan time. Cellular and all-informed (point-to-multipoint) architectures are examined. Cellular schemes based on UFDMA are also compared with Fixed Channel Assignment schemes and results demonstrate how DDCA schemes improve capacity. The allinformed net architectures are shown to be particularly susceptible to lognormal shadowing. Schemes are developed using the pictorial model which reduce the size of the vulnerable region; results show call dropping can be eliminated in non-shadowed environments if forward and reverse channels are paired, and checked for occupancy before use. The schemes can virtually eliminate call dropping in shadowed environments if additionally a minimum signal power threshold and variable transmitter power are used. Such schemes have capacity three times greater than the best previous UFDMA scheme.

DAVID GRACE DPHIL THESIS

COMMUNICATIONS RESEARCH GROUP, UNIVERSITY OF YORK

2

Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15

Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

16

1 Introduction

17

1.1

Civilian Wireless Personal Communications . . . . . . . . . . . . . . . .

17

1.2

Military Communications Operating Scenario . . . . . . . . . . . . . .

18

1.3

Overview of Channel Assignment Methods . . . . . . . . . . . . . . . .

20

1.4

Communication Architectures . . . . . . . . . . . . . . . . . . . . . . . .

22

1.5

Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

22

2 Distributed Dynamic Channel Assignment - A Literature Review 2.1

2.2

2.3

25

Practical Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25

2.1.1

The Coexistence Etiquette and PACS-UB . . . . . . . . . . . . .

25

2.1.2

DECT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

26

2.1.3

IS-136 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27

Interference and CIR Based Studies of DDCA . . . . . . . . . . . . . . .

27

2.2.1

˚ Akerberg Paper . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27

2.2.2

Foschini Paper . . . . . . . . . . . . . . . . . . . . . . . . . . . .

28

2.2.3

Work with DECT and GSM at the University of Leeds . . . . . .

29

2.2.4

Chuang Papers . . . . . . . . . . . . . . . . . . . . . . . . . . . .

29

Far East CIR Based Schemes . . . . . . . . . . . . . . . . . . . . . . . . .

32

2.3.1

Autonomous Reuse Partitioning . . . . . . . . . . . . . . . . . .

32

2.3.2

Channel Segregation . . . . . . . . . . . . . . . . . . . . . . . . .

33

2.3.3

Combined Autonomous Reuse Partitioning & Channel Segregation . . . . . . . . . . . . . . . . . . . . . . . . .

34

2.3.4

Power Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

35

2.3.5

Overview of Japanese Research . . . . . . . . . . . . . . . . . . .

36



3

CONTENTS

2.4

2.5

4

Bounds on the Performance of DDCA Algorithms . . . . . . . . . . . .

37

2.4.1

Cell based DCA for Single and Multiple Channels . . . . . . . .

37

2.4.2

Interference Based DCA Bounds . . . . . . . . . . . . . . . . . .

38

Miscellaneous and General Reviews . . . . . . . . . . . . . . . . . . . .

39

2.5.1

General Channel Assignment Review . . . . . . . . . . . . . . .

39

2.5.2

Market Driven Implications for DDCA . . . . . . . . . . . . . .

40

3 Simulation and Verification Methodology

42

3.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

42

3.2

Modelling of Communications Architectures . . . . . . . . . . . . . . .

43

3.3

Simulation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . .

44

3.3.1

UFDMA Simulation Using OPNET . . . . . . . . . . . . . . . . .

44

UFDMA Protocol Implemented in OPNET . . . . . . . . . . . .

46

UFDMA Simulation Using MATLAB . . . . . . . . . . . . . . .

47

Introduction to the Pictorial Model . . . . . . . . . . . . . . . . . . . . .

52

3.4.1

Basic Concepts of the Pictorial Model . . . . . . . . . . . . . . .

52

Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . .

56

3.5.1

Probability of Call Blocking . . . . . . . . . . . . . . . . . . . . .

56

3.5.2

Probability of Call Dropping . . . . . . . . . . . . . . . . . . . .

57

3.5.3

Probability of Call Unsuccessful . . . . . . . . . . . . . . . . . .

57

3.5.4

Call Aggressiveness . . . . . . . . . . . . . . . . . . . . . . . . .

58

3.5.5

Grade of Service . . . . . . . . . . . . . . . . . . . . . . . . . . .

58

3.5.6

Demanded Traffic and Offered Traffic . . . . . . . . . . . . . . .

59

3.5.7

Error Bars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

61

Validation using Analysis . . . . . . . . . . . . . . . . . . . . . . . . . .

62

3.3.2 3.4 3.5

3.6

3.6.1

3.7

Queueing Theory and its Applicability to Call Oriented Traffic . . . . . . . . . . . . . . . . . . . . . . . . .

62

Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

62

Erlang B Formula - M=M=m=m Queue . . . . . . . . . . . . . . .

62

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

65

4 Independent Link Channel Assignment

67

4.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

67

4.2

UFDMA Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

68

4.2.1

69

First Available . . . . . . . . . . . . . . . . . . . . . . . . . . . . .



CONTENTS

5

4.2.2

Least Interfered Channel . . . . . . . . . . . . . . . . . . . . . . .

69

4.2.3

Hysteresis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

70

4.2.4

Call Reassignment . . . . . . . . . . . . . . . . . . . . . . . . . .

72

4.3

Simulation Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

72

4.4

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

74

4.4.1

Call Blocking and Dropping Probability . . . . . . . . . . . . . .

74

4.4.2

Probability Call Unsuccessful and Aggressiveness . . . . . . . .

76

4.4.3

Interference Threshold . . . . . . . . . . . . . . . . . . . . . . . .

76

4.4.4

Hysteresis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

82

4.4.5

Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

84

Channel Selection at the Receiver . . . . . . . . . . . . . . . . . . . . . .

86

4.5.1

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

86

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

89

4.5 4.6

5 Dynamic Channel Assignment using ‘All-Knowing’ Algorithms

91

5.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

91

5.2

All-Knowing Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . .

92

5.2.1

Link-Gain Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . .

93

5.2.2

Quasi-Optimum Assignment . . . . . . . . . . . . . . . . . . . .

95

Link Ordering Strategy . . . . . . . . . . . . . . . . . . . . . . .

97

No Call Dropping Assignment . . . . . . . . . . . . . . . . . . .

98

5.2.3 5.3

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.3.1

Simulation Validation . . . . . . . . . . . . . . . . . . . . . . . . 100

5.3.2

Effect of Channel Ordering . . . . . . . . . . . . . . . . . . . . . 101

5.3.3

Effect of Shadowing on Performance . . . . . . . . . . . . . . . . 104

5.3.4

Effect of Increasing the Number of Start Link Permutations . . 104

5.4

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

5.5

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

6 Multichannel CSMA

110

6.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

6.2

Conventional CSMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 6.2.1

Analysis of Non-Persistent CSMA . . . . . . . . . . . . . . . . . 112

6.2.2

Other Types of CSMA . . . . . . . . . . . . . . . . . . . . . . . . 115



CONTENTS

6

6.3

Multichannel CSMA Modelling Scenario and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

6.4

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

6.5

6.4.1

Fixed Call Durations . . . . . . . . . . . . . . . . . . . . . . . . . 121

6.4.2

Variable Call Durations . . . . . . . . . . . . . . . . . . . . . . . 121

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

7 DDCA Over Paired Duplex Channels

126

7.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

7.2

Review of UFDMA IA Algorithm . . . . . . . . . . . . . . . . . . . . . . 127

7.3

UFDMA PDA Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

7.4

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

7.5

Simulation Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

7.6

Results for UFDMA IA and UFDMA PDA . . . . . . . . . . . . . . . . . 131

7.7

Sensitivity Analysis of UFDMA . . . . . . . . . . . . . . . . . . . . . . . 133 7.7.1

Environmental Factors . . . . . . . . . . . . . . . . . . . . . . . . 134 Path Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 Lognormal Shadowing . . . . . . . . . . . . . . . . . . . . . . . . 137

7.7.2

Call Failure with Link Length . . . . . . . . . . . . . . . . . . . . 137

7.7.3

Measurement Error in Level Measurements . . . . . . . . . . . . 140 Interference Level Measurement Error . . . . . . . . . . . . . . . 140 SNR Measurement Error . . . . . . . . . . . . . . . . . . . . . . . 144

7.8

Relative Performance of DCA/DDCA Algorithms . . . . . . . . . . . . 144

7.9

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

8 DDCA for Cellular and All-Informed Net Architectures

151

8.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

8.2

Cellular Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 8.2.1

PACS-UB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

8.2.2

UFDMA for a Cellular Architecture . . . . . . . . . . . . . . . . 156 Fixed Channel Assignment Comparison . . . . . . . . . . . . . . 156 The Engset Distribution . . . . . . . . . . . . . . . . . . . . . . . 161 Simulation Validation with Analysis . . . . . . . . . . . . . . . . 163



CONTENTS

7

Performance of UFDMA PDA Cellular . . . . . . . . . . . . . . 164 8.3

8.4

Point-to-Multipoint Communication - All-Informed Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 8.3.1

Modelling Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . 169

8.3.2

Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

8.3.3

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

9 Call Dropping Reduction in Shadowed Environments

175

9.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

9.2

Incorporating the MINRX Threshold into UFDMA PDA . . . . . . . . . 176 9.2.1

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177

9.3

Effect of Additional Interfering Node on the Pictorial Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

9.4

Improving the Link Margin Using Variable Transmitter Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 9.4.1

UFDMA Power Control Algorithm 1 . . . . . . . . . . . . . . . . 185

9.4.2

UFDMA Power Control Algorithm 2 . . . . . . . . . . . . . . . . 188 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

9.4.3

UFDMA Power Control Algorithm 3 . . . . . . . . . . . . . . . . 195 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

9.5

9.4.4

UFDMA Power Control Algorithm using Autonomous Reuse Partitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201

9.4.5

Performance of Power and Non-Power Control Algorithms . . 202

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202

10 Further Work

205

10.1 DDCA with Power Control . . . . . . . . . . . . . . . . . . . . . . . . . 206 10.2 Bandwidth on Demand and Service Adaptation . . . . . . . . . . . . . 206 10.3 Adaptive Modulation and Coding . . . . . . . . . . . . . . . . . . . . . 207 10.4 Robustness to Interference and Spoofing . . . . . . . . . . . . . . . . . . 208 10.5 Directional Antennas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 10.6 The Effect of Node Mobility on DDCA . . . . . . . . . . . . . . . . . . . 209 10.7 Channel Scan Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 11 Summary and Conclusions


210


CONTENTS

8

11.1 Novel Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 A Publications

215

A.1 IEEE MILCOM’95 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 A.2 IEE Colloquium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 A.3 IEE ICPMSC’96 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 A.4 Electronics Letters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 A.5 IEEE ICUPC’97 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 A.6 IEE International Conference on Simulation ’98 . . . . . . . . . . . . . . 238 A.7 IEEE GLOBECOM’98 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 A.8 IEEE Communciations Letters (submitted) . . . . . . . . . . . . . . . . 250 A.9 IEE Proceedings Communications (submitted) . . . . . . . . . . . . . . 253 B Glossary


259


List of Figures 1.1

Cellular Frequency Reuse Plans for Two Cluster Numbers (clusters are highlighted). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21

3.1

A typical OPNET model, illustrating the three editor views . . . . . . .

45

3.2

Normal Setup and Clear Down phase transitions . . . . . . . . . . . . .

48

3.3

Blocked Call phase transitions . . . . . . . . . . . . . . . . . . . . . . . .

49

3.4

Dropped Call phase transitions . . . . . . . . . . . . . . . . . . . . . . .

50

3.5

A typical MATLAB UFDMA simulation ‘engine’. . . . . . . . . . . . . .

51

3.6

The effect of tight and loose interference threshold on size of interference exclusion area. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53

3.7

The Effect of Link Length on SNR Exclusion Area . . . . . . . . . . . .

54

3.8

Pictorial model for a single UFDMA link . . . . . . . . . . . . . . . . . .

55

3.9

A Markov Representation of the Erlang B formula (M=M=m=m queue). 63

3.10 Probability of Blocking v Offered Traffic per Server using the Erlang B Formula for Different Numbers of Servers. . . . . . . . . . . . . . . . .

66

4.1

A typical point-to-point communication link. . . . . . . . . . . . . . . .

68

4.2

Flow Chart Illustrating the Simulation Algorithm for UFDMA using First Available Channel. . . . . . . . . . . . . . . . . . . . . . . . . . . .

70

Flow Chart Illustrating the Simulation Algorithm for UFDMA using Least Interfered Channel. . . . . . . . . . . . . . . . . . . . . . . . . . .

71

Flow Chart Illustrating the Simulation Algorithm for UFDMA using Least Interfered Channel with Call Reassignment. . . . . . . . . . . . .

73

Blocking and Drop Call Probability for the UFDMA Algorithms, Interference Threshold -100dBm. . . . . . . . . . . . . . . . . . . . . . . .

75

Probability Call Unsuccessful and Aggressiveness for the UFDMA Algorithms, Interference Threshold -100dBm. . . . . . . . . . . . . . . . .

77

Effect of Interference Threshold on Number of Demanded Calls Supported for a Maximum Call Blocking Probability of 9%. . . . . . . . . .

78

Effect of Interference Threshold on Number of Demanded Calls Supported for a Maximum Call Dropping Probability of 9%. . . . . . . . .

79

4.3 4.4 4.5 4.6 4.7 4.8



9

LIST OF FIGURES

4.9

10

Effect of Interference Threshold on Number of Demanded Calls Supported for a Maximum Probability of Call Unsuccessful of 9%. . . . . .

79

4.10 Pictorial representation of UFDMA illustrating the effects of a tight and loose interference threshold. . . . . . . . . . . . . . . . . . . . . . .

80

4.11 Geographical layout of nodes sharing the same channel at two interference thresholds. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

81

4.12 Effect of Hysteresis in SNR on Number of Demanded Calls Supported for Maximum Blocking and Call Unsuccessful Probabilities of 5%, and Dropping Probabilities of 5% and 15% for the UFDMA LIC Algorithm with Interference Threshold -100dBm. . . . . . . . . . . . . . . . . . . .

82

4.13 Pictorial representation of UFDMA illustrating the effects of hysteresis in MINSNR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

83

4.14 Average Number of Successful Call Reassignments per New Call Accepted against Number of Demanded Calls for Different Interference Thresholds and Number of Channels on the Forward/Reverse Link. .

85

4.15 Drop Call Probability against Number of Demanded Calls for Channel Assignment performed at the transmitter or receiver for interference thresholds of -120dBm and -70dBm. . . . . . . . . . . . . . . . . . . . .

87

4.16 Exclusion Area Representation for a Specific Link assuming Interference Limited Conditions. . . . . . . . . . . . . . . . . . . . . . . . . . . .

88

4.17 Blocking Probability against Number of Demanded Calls for Channel Assignment performed at the transmitter or receiver for interference thresholds of -120dBm and -70dBm. . . . . . . . . . . . . . . . . . . . .

88

5.1 5.2

Flow Chart Illustrating the Simulation Algorithm for ‘Quasi-Optimum’ Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

96

Flow Chart Illustrating the Simulation Algorithm for No Call Dropping All-Knowing Algorithm. . . . . . . . . . . . . . . . . . . . . . . . .

99

5.3

Comparison of the All-Knowing NCD algorithm with the Erlang B formula for different numbers of channels (‘o’ simulation results). . . . 100

5.4

Probability Call Unsuccessful and Aggressiveness for the All-Knowing Algorithms in the absence of lognormal shadowing. . . . . . . . . . . . 101

5.5

Blocking and Drop Call Probability for the All-Knowing Algorithms in the absence of lognormal shadowing. . . . . . . . . . . . . . . . . . . 103

5.6

Probability Call Unsuccessful and Aggressiveness for the All-Knowing Algorithms in the presence of correlated lognormal shadowing std 8dB. 105

5.7

Blocking and Drop Call Probability for the All-Knowing Algorithms in the presence of correlated lognormal shadowing std 8dB. . . . . . . 106

5.8

The effects of start link permutations on the Blocking and Drop Call Probability for the All-Knowing Algorithms in the absence of lognormal shadowing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107



LIST OF FIGURES

11

5.9

The effects of start link permutations on the Blocking and Drop Call Probability for the All-Knowing Algorithms in the presence of correlated lognormal shadowing std 8dB. . . . . . . . . . . . . . . . . . . . . 108

6.1

Channel timing diagram illustrating the phases of non-persistent CSMA (all parameters are normalised to the fixed packet period of To s, where To s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

=1

6.2

Throughput against Offered Traffic Behaviour of Single Channel NonPersistent CSMA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

6.3

The effects of channel sensing delay on length of the call busy period. . 119

6.4

Comparison between analysis and simulation for M-CSMA, fixed call duration ( s), sc ; ts ms. . . . . . . . . . . . . . . . . . . . . . . . . 122

6.5

Comparison between analysis and simulation for M-CSMA, exponentially distributed call durations ( s), sc ; ts ms. . . . . . . . . . . . 123

6.6

Comparison between analysis and simulation for M-CSMA, exponentially distributed call lengths ( s), sc : ms, ts ms. . . . . . . . 124

6.7

Comparison between analysis and simulation for M-CSMA, exponentially distributed call durations ( s), sc ms, ts ms. . . . . . . 125

7.1

A typical point-to-point communication link. . . . . . . . . . . . . . . . 127

7.2

Pictorial Model Representation of one half of a link of UFDMA IA. . . 128

7.3

Pictorial Model Representation of UFDMA PDA. . . . . . . . . . . . . . 129

7.4

Comparison between the independent and paired duplex channel protocols with no shadowing for different interference thresholds and MINSNR 10dB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

7.5

Comparison between the independent and paired duplex channel protocols with lognormal shadowing standard deviation 8dB for different interference thresholds and MINSNR 10dB. . . . . . . . . . . . . . . . . 133

7.6

The effect of path loss exponent variation on grade of service for UFDMA PDA and UFDMA IA point-to-point architectures. . . . . . . . . . . . . 135

7.7

The effect of interference threshold variation on quality of service for UFDMA PDA and UFDMA IA point-to-point architectures, with path loss exponents of 3 and 4. . . . . . . . . . . . . . . . . . . . . . . . . . . 136

7.8

The effect of lognormal shadowing standard deviation variation on quality of service for UFDMA PDA and UFDMA IA point-to-point architectures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

7.9

Call Failure with Link Length with a restricted results area. . . . . . . . 139

1

=0 =1

1

=0 =1

=1

=1

=01

=1

=1

=1

7.10 Call Failure with Link Length with results gathered over the whole of the simulation area. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140



LIST OF FIGURES

12

7.11 The effect of error on interference level measurement for UFDMA IA and PDA using point-to-point architectures in the absence of Lognormal Shadow Fading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 7.12 The effect of error on interference level measurement for UFDMA IA and PDA Algorithms using point-to-point architectures with Lognormal Shadow Fading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 7.13 The effect of error on SNR measurement for UFDMA IA and PDA Algorithms using point-to-point architectures with Lognormal Shadow Fading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 7.14 Comparative performance of the UFDMA and All-Knowing Algorithms without shadowing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 7.15 Comparative performance of the UFDMA and All-Knowing Algorithms in the presence of correlated lognormal shadowing std 8dB. . . . . . . 148 8.1

PACS-UB Statistics Verification No Shadowing, 0.2E per portable. Comparison with results in [1] and Figure 8.2 . . . . . . . . . . . . . . . . . . 155

8.2

Results Presented by Chang[1] for a PACS-UB indoor scenario with port spacings of 10m and 20m. . . . . . . . . . . . . . . . . . . . . . . . 157

8.3

PACS-UB Statistics Verification Shadowing std 8dB, 0.2 E per portable. 158

8.4

Cluster Patterns with different numbers of cells per cluster. . . . . . . . 159

8.5

Frequency reuse behaviour of a uniform layout of hexagonal cells. . . 160

8.6

Call Arrival and Departure Parameter Definitions, when calls are accepted or blocked. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

8.7

The state diagram which is used to derive the Engset Distribution. . . 162

8.8

Comparison between Engset distribution and Erlang B formula for different numbers of nodes. . . . . . . . . . . . . . . . . . . . . . . . . . 164

8.9

Comparison between Engset distribution and Cellular FCA Schemes. . 165

8.10 Performance of UFDMA Cellular for different Interference Thresholds, with a channel allocation of 28 channels. . . . . . . . . . . . . . . . 166 8.11 Comparison of cellular FCA and DDCA channel assignment with shadowing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 8.12 Typical Layout of an All-Informed Net with 6 nodes. . . . . . . . . . . 169 8.13 Blocking probability performance obtained by simulation in the absence of lognormal shadowing for different numbers of nodes per net for 10 channels, Interference Threshold -70dBm. . . . . . . . . . . . . . 172 8.14 Comparison of the semi-analytical derivation of blocking probability and simulation results for 10 channels, Interference Threshold 70dBm, Shadowing std 8dB. . . . . . . . . . . . . . . . . . . . . . . . . . 173 8.15 Dropping Behaviour of the All-Informed Net, for 10 Channels, Interference Threshold -70dBm and Lognormal Shadowing 8dB. . . . . . . 173 DAVID GRACE DPHIL THESIS


LIST OF FIGURES

13

9.1

Effect of Lognormal Shadowing on the Pictorial Model (shadowing space). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178

9.2

Effect of Minimum Received Power Threshold (MINSNR + Int Threshold) on Blocking and Dropping Performance for Interference Thresholds of -120dBm and -110dBm, link length 400-4000m. . . . . . . . . . . 179

9.3

Effect of Minimum Received Power Threshold (MINSNR + Int Threshold) on Blocking and Dropping Performance including comparison with the Independent Algorithm, link length 400-4000m. . . . . . . . . 180

9.4

The Effect on Interference Levels as the number of Transmitting Nodes changes during the Lifetime of a Call. . . . . . . . . . . . . . . . . . . . 182

9.5

Effect of power control with unrestricted transmit power range. . . . . 187

9.6

Effect of power control with constrained transmit power range, with maximum power determined from difference in dB between interference threshold and interference level. . . . . . . . . . . . . . . . . . . . 189

9.7

The effects of different interference thresholds and initial transmit powers on call blocking and dropping performance in the presence of lognormal shadowing std 8dB. . . . . . . . . . . . . . . . . . . . . . . . . . 193

9.8

Value of SNR when calls are dropped when MINSNR is 10dB for several interference thresholds and initial transmit powers in the presence of lognormal shadowing std 8dB. . . . . . . . . . . . . . . . . . . . . . . 194

9.9

The final median transmitter power of a successful call against both initial transmit power and Imax Pmax for different interference thresholds in the presence of lognormal shadowing std 8dB. . . . . . . 196

9.10 The effects of different interference thresholds and initial transmit powers on call blocking and dropping performance in the presence of lognormal shadowing std 8dB. . . . . . . . . . . . . . . . . . . . . . . . . . 198 9.11 Value of SNR when calls are dropped with MINSNR at 10dB for different interference thresholds and initial transmit powers in the presence of lognormal shadowing std 8dB. . . . . . . . . . . . . . . . . . . . . . . 199 9.12 The final median transmitter power of a successful call against both initial transmit power and Imax Pmax for different interference thresholds in the presence of lognormal shadowing std 8dB. . . . . . . 200

9.13 Blocking and dropping performance of the UFDMA ARP algorithm in the presence of lognormal shadowing. . . . . . . . . . . . . . . . . . . . 201 9.14 Comparative Performance between UFDMA PDA and UFDMA PDA Power Control Algorithm 2 in the presence of correlated lognormal shadowing std 8dB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203



List of Tables 7.1

Relative performance of the UFDMA and All-Knowing Algorithms for point-to-point links at the GOS Threshold with and without shadowing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

8.1

PACS-UB Parameters used in the Simulation. . . . . . . . . . . . . . . . 154

8.2

The effects of Efficiency Loss for Multiple Operators at 5% Blocking Probability, 832 channels, Poisson traffic[2]. . . . . . . . . . . . . . . . . 159

8.3

The effect of number of cells in a cluster on frequency reuse distance (determined assuming a base station-mobile distance of 5km) and Carrier to Interference ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

9.1

Increase in interference due to additional activations close to edge of IEA (first ring) around a node of interest. . . . . . . . . . . . . . . . . . 183

9.2

Increase in interference due to additional activations in the second around a node of interest, in addition to a node located on the IEA. . . 184

9.3

The effects of Imax Pmax on offered traffic limit for the required quality of service parameters in the presence of correlated lognormal shadowing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197



14

Acknowledgements I would firstly like to thank the Defence Evaluation and Research Agency (DERA) Malvern for funding this work. I would also like to thank in particular Dave Camm, Rick Barfoot, and Colin Davies all from DERA for the many interesting discussions on the work. I am grateful to my two supervisors Tim Tozer and Dr Alister Burr, for the constant encouragement, and without which much of this work would not have been possible. Further thanks go to the other members of the Communications Research Group for providing a pleasant atmosphere in which to work. Thanks also to MIL3, the developer of OPNET, and MathWorks for the development of MATLAB, for providing excellent simulation packages. I am also grateful to the developers and maintainers of LATEXfor providing a document processing package which greatly simplifies the formatting of large documents. Finally, I would also like to thank my Father for proof-reading this thesis.



15

Declaration Some of the research in this thesis has resulted in publications in journals and conference proceedings. These papers are included in Appendix A. All contributions presented in this thesis as original are as such to the best knowledge of the author. References and acknowledgements to other researchers in the field have been given as appropriate.



16

Chapter 1 Introduction Contents 1.1

Civilian Wireless Personal Communications . . . . . . . . . . . . . . 17

1.2

Military Communications Operating Scenario . . . . . . . . . . . . 18

1.3

Overview of Channel Assignment Methods . . . . . . . . . . . . . . 20

1.4

Communication Architectures . . . . . . . . . . . . . . . . . . . . . . 22

1.5

Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

1.1 Civilian Wireless Personal Communications Wireless communications have changed beyond recognition over the last 15 years. Prior to the development of the cellular mobile phone, typically only professionals used radio communications, for example the emergency services, taxi drivers, military, and television and radio. The first widely used cellular mobile phones started to appear in the early 1980s, were analogue and installed initially in cars due to the bulky hardware required at the time. Shortly after hand held devices weighing more than 1 kg became available. These were based on AMPS (American Mobile Phone System) in the USA and later appeared in the UK as TACS which was a derivative of AMPS [3]. The development and deployment of second generation systems took place from the late 1980s to the present day. These were digital rather than analogue providing the end user with supposedly better voice quality speech, whilst providing the operators with considerable improvements to the capacity per unit bandwidth. The other advantage of second generation systems is their roaming ability. The pan-European standard GSM (Global System for Mobile Communications) allowed international roaming for the first time throughout Europe. GSM has now been adopted by many other countries throughout the world. The USA adopted an evolutionary approach to its AMPS system, developing D-AMPS (Digital AMPS). Also, a second standard is also used in the USA (IS-95), developed by Qualcomm Inc, which provides an air interface based on CDMA. DAVID GRACE DPHIL THESIS


17

CHAPTER 1. INTRODUCTION

18

Wireless communications were also developed for cordless telephone applications, where users had a personal ‘base’ in their homes connected to a land-line. An early standard in the UK and Canada was CT2, with its digital second generation counterpart DECT (Digital Enhanced Cordless Telephone). In the USA there are at least two cordless standards: PACS-UB, and IS-136. PACS-UB will be mentioned later and is primarily intended for a wireless PABX scenario, with multiple ports providing overlapping coverage areas, allowing portables to switch connections frequently between ports. The Japanese have a similar system called PHS (Personal Handy phone System). The third area attracting considerable interest is Fixed Wireless Access (FWA), or alternatively known as Wireless in the Local Loop (WLL), which is intended to replace the cable from the ‘last mile to the home’. In Britain, this is currently being marketed by Ionica (amongst others), in what is becoming a highly competitive market. Where FWA will probably prove most successful is in low density communication scenarios where cost of cabling is relatively high, and in the developing world where cabling infrastructure is not yet in place. Third generation systems are currently under research worldwide, and are being designed to support full multimedia access. A worldwide standard may be achieved, the so-called FPLMTS (Future Public Land Mobile Telecommunication Systems). This is currently being worked on in Europe as UMTS (Universal Mobile Telephone Standard). The computer industry is developing Wireless IP (Internet Protocol) and Wireless ATM, and shortly satellite mobile phone systems such as Iridium will be available, providing worldwide coverage. This diversity suggests that a single unifying standard may be difficult to realise, and many people suggest that it could be harmful as it will stifle future innovation. In practice, what will probably emerge is a multi-system standard supporting several different air interfaces and transmission protocols, all supported by the mobile unit with appropriate software and hardware.

1.2 Military Communications Operating Scenario Military wireless communications (for the army in particular) have traditionally operated quite differently from their civilian counterparts, and have been around in some form or other for at least half a century. The differences are a result of specific requirements, which are:

Security This is normally achieved using encryption/decryption devices which require up to half a second synchronisation, which is why traditionally FDMA systems have been used, rather than the more flexible F/TDMA systems used in the civilian arena. In addition, added security may be provided through spread spectrum and frequency hopping schemes.




19

Redundancy The ability to cope with destruction or failure of parts of a communication system is of paramount importance. Typically, military communications have two backup systems. These include a complete duplicate of the equipment at the base, with the operating unit and backup devices in different locations. These systems are both kept powered up, with active and backup units changing roles every hour or so. Redundancy is also incorporated into the frequency allocation. Each unit is given three frequencies (when central frequency planning is used); one is for general use and a second in the same frequency band as backup against interference or jamming. The third frequency is normally in the HF band and used for emergency transmissions. No single points of failure A single point of failure is a considerable weakness in a military communications system because it allows the enemy to concentrate resources on destroying the weak point, thereby seriously affecting all users. Single points of failure could include cellular base stations if insufficient redundancy is present. Traditionally data transmission has mitigated this problem by using Packet Radio, with the British Military using Packet Radio Network (PRN). Low probability of interference/detection (LPI/LPD) This is often required in order to reduce the vulnerability to attack. If transmissions can be detected then it can be possible to determine the source of transmission, leaving the system open to attack. Ways to mitigate this problem include spread spectrum/frequency hopping, as well as changing the geographical location of the base. Ability to contact different numbers of users simultaneously This concept is also required on a small scale in the civilian area by taxi drivers and the emergency services and is referred to as Private Mobile Radio (PMR). The military call the communication method an ‘All Informed Net’ with the current British Military system being Combat Net Radio (CNR) 1 .

Communications normally operate through the chain of command with messages relayed between users. Messages are passed from corps level, to battle group, to brigade, to division and vice versa, with messages taking some time to get from one end to the other. Communications often have to be passed by hand to a different geographical location in order to be sent up or down the chain of command. It only became apparent in the Gulf War that the civilian technology capabilities were now better at transferring information than the military technologies. For instance there were several reports that US President George Bush kept in touch with developments in the Middle East using CNN, supplementing information available from his military [4]. This led to a re-evaluation in the West of the requirements of military communications. It was decided that it was now preferable wherever possible to use civilian Commercial Off The Shelf (COTS) products and standards, and 1

Other voice communications are handled by a UK system called BOWMAN, which handles wireless trunk communications.




20

adapting them for military use. This provided the benefits of considerably reduced development costs, whilst also exploiting the considerable wealth of experience now available in the civilian communications arena. With the advent of multimedia, it is envisaged that a considerably lower proportion of messages will be speech, with full multimedia capability available to each soldier in the field. For instance up-to-date maps showing locations of military installations, and video footage of the battle field obtained from troops and vehicles could be sent, along with information about the condition of resources in a given area. There have also been reports that a typical soldier of the future will be similar to ‘Robocop’ with an eye level display and video camera in his helmet. Issues other than achieving reliable communications will also be important to the end user, the main one being information overload. Considerable care will have to be taken that only appropriate information is sent to the user, otherwise his performance will be impaired.

1.3 Overview of Channel Assignment Methods Early channel assignment schemes relied on Fixed Channel Assignment (FCA). Frequency planning is based on geography and locations of base stations. The frequency allocation2 is then divided into assignments[5] (typically 3, 4, 7, 9, 11) and an assignment given to each base station. Each frequency assignment is reused a suitable distance away from each another such that mutual interference is kept below an acceptable level. The number of cells over which the allocation is used is called a cluster, with the number of cells in the cluster referred to as the cluster size, or cluster number. Figure 1.1 shows a cellular frequency plan with cluster numbers of 3 and 7. The greater the number of assignments into which the allocation is split (causing each assignment to contain fewer channels), the less often the frequency has to be reused and the lower the level of interference. Fewer channels available in each cell lead to greater trunking inefficiencies3 and lower capacities. Dynamic Channel Assignment (DCA) schemes were developed to solve the trunking efficiency problem, by ensuring that every channel in the allocation was potentially available for use. DCA enables all channels to be available for selection at base station and these are typically allocated on a call-by-call basis. This has the added advantage that such systems are able to cope much better with fluctuating traffic demands, both temporally and spatially. Most DCA schemes are centrally controlled, that is information is passed to and from cell base stations to the central controller, from where the controller then determines which channels are available for use. Many of the schemes use a tiling algorithm which reuse frequencies depending on how they are being used, both in neighbouring cells and cells separated by 2

The frequency allocation is defined as a discrete band of the radio spectrum which has been determined by a regulator. The frequency assignment is the frequency or frequencies on which a particular user (e.g. basestation) is allowed to operate within a given frequency allocation[5]. 3 When fewer channels are available in each cell then, for the same traffic load per channel, the probability that at least one channel is available for use is decreased. Such effects are discussed in more detail in Subsubsection 8.2.2. DAVID GRACE DPHIL THESIS



21

1 1 1 1

1 2

3

2

2 3

(a) 3

1 3

4

2

5

7 3

1 2

6

4 6 5

7 3

5

7 3

5

7 3

1

3

2

2

2

6

6

4

4

(b) 7

Figure 1.1: Cellular Frequency Reuse Plans for Two Cluster Numbers (clusters are highlighted). greater reuse distances. Such schemes are shown to work well in uniform environments with regular cell sizes, but may perform poorly in real environments. Real environments are non uniform, due to differing cell sizes, and are often subject to effects such as shadowing and external sources of interference. A much smaller number of schemes actually report back interference and CIR measurements, which are then used to determine which channels are used. These are obviously much more complicated and require considerably more control information to be transferred, but are likely to provide an assignment which yields higher capacity in non uniform environments. The final methods, and those covered by this thesis, are Distributed Dynamic Channel Assignment (DDCA) schemes. DDCA schemes typically use local interference and CIR conditions to determine the most suitable channel, with each base station or node operating independently. This removes the control overhead required by a centralised scheme. In addition, unlike FCA, DDCA allows the assignment to be varied in response to changing localised conditions, whilst at the same time removing a single point of failure. A possible drawback of DDCA schemes is that with the absence of centralised control, new call arrivals can at times cause excessive interference on other links. This particular problem is investigated in depth in Chapters 7 and 9 of this thesis along with possible methods of reducing the problem, but without the use of central control.




22

1.4 Communication Architectures This thesis examines possible Distributed Dynamic Channel Assignment (DDCA) techniques for military communications architectures. It is not suggested that the DDCA techniques covered in later chapters conform to every military communications requirement. Instead, two features have been selected: no single points of failure, and the ability to talk to differing numbers of users simultaneously. The term architecture is used to describe the node configuration and connectivity; the following communications architectures are examined in the thesis:

Point-to-point This architecture is fully distributed; that is each node, or pair of nodes, determines its own channel assignment. This can be considered as being a mobilemobile type link; a typical scenario would be two soldiers talking to each other out in the field. It is also very similar to a civilian cordless architecture, where one node is the base unit and the other node the handset. In this thesis the node originating the call is referred to as the Caller (CR) node, with the recipient being the Callee (CE), or called node. This architecture is the most extensively studied of the three covered in this thesis. All-informed net This is a point-to-multipoint architecture, where everyone can listen to every communication. This is most commonly used by the military, but with the exception of PMR applications it is rarely used by civilians. Cellular This is currently rarely used by the military but it is expected to form the basis of many future applications due to the technology transfer taking place between the civilian and military. This architecture allows point-to-point voice communications to take place, but differs from the first architecture in that the mobile can connect to the most suitable base station. Mobile-to-mobile calls would be routed through one or more base stations and probably a dedicated fixed link, e.g. a land line. It is envisaged that base stations in a military scenario could be portable, located in a moving vehicle. Another idea would be to parachute base stations into the desired area, and allow them to configure themselves to local propagation conditions. Parachutes are currently one method of deploying localised jammers.

1.5 Thesis Outline This thesis explores different DDCA methods, not because they necessarily yield greater capacity, but because they can remove any single point of failure in a system, and at the same time eliminate the need for frequency planning. The ability for




23

the military to quickly deploy communications facilities represents a considerable advantage over their current schemes which requires prior frequency planning and allows little adaptation to changing conditions. Chapter 2 presents a literature review which summarises, and comments on, papers that investigate DDCA. Chapter 3 describes the simulation methodology used to assess the DDCA schemes throughout this thesis. Simulation is used extensively, since the operation of DDCA in many of the chosen scenarios is too complex to fully analyse mathematically. However, the use of such scenarios is critical to provide a more realistic evaluation of the performance of DDCA. A discussion of how performance should be measured is also presented. Chapter 4 investigates the effects on performance of several parameters present in some of the early DDCA schemes developed for the point-to-point architecture. The differing performances are discussed with the aid of a novel pictorial model which allows the effects of interference and Carrier to Interference Ratio (CIR) to be graphically represented. Several DCA algorithms are developed in Chapter 5 for a pointto-point architecture as a way of determining the maximum capacity of a scenario. These are then used to compare the performance of the DDCA algorithms. These algorithms are referred to as ‘All-Knowing’ since they capture all conditions present at the nodes in the system, and are not intended to be realisable in practice as channel assignment protocols. These differ from many algorithms in the literature in that they are not cellular based, and take into account non uniform conditions and shadowing. Performance is also measured in the same way as that used for the DDCA schemes, so allowing easy comparison. The similarities between DDCA and packet based channel assignment schemes are covered in Chapter 6. It is shown that Carrier Sense Multiple Access (CSMA) is very similar to DDCA schemes, suffering from both the ‘vulnerable’ period and hidden terminal problem. The temporal effects of a finite call set-up time on the number of call failures is estimated using mathematical analysis adapted from packet oriented Multichannel CSMA scenario. Importantly the analysis shows that there can be a limit on performance as the number of channels increases. The lessons learnt from the early DDCA schemes developed in Chapter 4 are used as a basis for the next generation of DDCA scheme which have paired forward and reverse channels, and they are investigated in Chapter 7. The schemes are designed using the pictorial model to have particular predicted behaviour. Again, performance is verified by means of simulation in both shadowing and non shadowing environments. Chapter 8 applies DDCA to cellular and all-informed net architectures. The DDCA cellular algorithms, under high activity per node, are compared both with FCA algorithms and the Engset Distribution. Simulation is used to assess the performance of DDCA for the two architectures both with and without lognormal shadowing. In Chapter 9 several modifications, which may further improve dropped call performance, are then investigated, including the incorporation of a variable minimum received power threshold and power control. The pictorial model is also further




24

developed. In Chapter 10 ideas for future work are discussed, and finally Chapter 11 presents the main conclusions of the thesis and identifies the novel contributions made.



Chapter 2 Distributed Dynamic Channel Assignment - A Literature Review Contents 2.1

Practical Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.2

Interference and CIR Based Studies of DDCA . . . . . . . . . . . . . 27

2.3

Far East CIR Based Schemes . . . . . . . . . . . . . . . . . . . . . . . 32

2.4

Bounds on the Performance of DDCA Algorithms . . . . . . . . . . 37

2.5

Miscellaneous and General Reviews . . . . . . . . . . . . . . . . . . 39

2.1 Practical Systems There are several practical systems which employ distributed channel assignment. The most common ones are PACS-UB, DECT and IS-136, and are all intended for the cordless/PABX environment, where the portable to port (base station) distance is typically less than 500m.

2.1.1 The Coexistence Etiquette and PACS-UB A detailed discussion of the development of the coexistence etiquette is presented by Steer [6], and has been adopted by the FCC [7]. The coexistence etiquette is defined in FCC 47 CFR Part 15 and allows users to share common bandwidth between 19101930 MHz. The aim of the etiquette is not to set frame durations, signalling protocols, and transmission bandwidths, but instead, to encourage innovation, the etiquette sets limits on the worst case interference scenario by ensuring that a listen-beforetransmit protocol is used; users have limited transmit power and limited time duration transmissions. The allocation is divided into two sub-bands, one for isochronous



25

CHAPTER 2. DISTRIBUTED DYNAMIC CHANNEL ASSIGNMENT - A LITERATURE REVIEW

26

(speech) traffic, and the other for asynchronous potentially wideband data. Frequency selection for the narrowband speech traffic takes place in order starting at the low frequency end of the allotted spectrum. With the asynchronous sub-band, lower bandwidth traffic is allocated at the edges of the sub-band if locations are free, with the centre of the band being left for higher bandwidth traffic. The Personal Access Communications System - Unlicensed Band (PACS-UB) is a practical system which uses the coexistence etiquette. This is currently under development by Bellcore and is described in [1, 8]. A brief description of the protocol is given and then simulation results are presented for a PABX in building scenario where the port-port distances are 5-10m. It is envisaged with this scheme that handoffs will occur often, with ports providing overlapping coverage areas. The main reason for this design is that it is difficult to carry out detailed frequency planning within buildings at the 1.9GHz frequency due to the complex interaction with objects (which often change location). With the scheme it is intended that ports will choose a suitable frequency using the First Available (FA) channel with interference below a given threshold. The threshold is determined for the particular environment and port density, such that all ports can obtain a channel, while minimising the amount of interference caused by frequency reuse. The ports can support up to 4 conversations at a time using a TDMA/TDD frame structure. Portables connect to the strongest port, but if this is fully occupied then the port with the next strongest signal will be chosen and so on until at most 10 ports have been tried. This scheme is considered in more detail in Chapter 8.

2.1.2 DECT A pan-European standard DECT (Digital European Cordless Telephone) [3] has been adopted for the next generation of cordless telephones. This is based on a FDMA / TDMA / TDD frame structure with an allocation of 120 channels, made up of 10 frequencies each supporting up to 12 conversations. The number of transceivers in a base station is usually limited; typically the simplest base station will contain 1 transceiver capable of operating on any frequency, in any time slot. More complicated base stations could contain up to 10 transceivers each capable of choosing any one of the 10 frequencies operating in each of the time slots[3]. Channel assignment is similar to other schemes, but with channel selection taking place at the mobile only. The portable locks on to the strongest base station by examining the beacon frequencies transmitted on a control channel from the base station. The mobile then scans sequentially the 120 channels and selects the one that will have the highest CIR. The base station is notified of the chosen channel, which is then used for the subsequent conversation. The performance of the DECT channel assignment scheme is compared with other distributed schemes (e.g. the one developed by Bellcore) in [9]. DECT has also been proposed to form part of a wireless LAN [10] and also for Fixed Wireless Access (FWA) [11].




27

2.1.3 IS-136 A third scheme currently under development is based on the US IS-136 standard. The design and capacity of an IS-136 personal base station are discussed in [12, 13]. The emphasis in both of these papers is on the development of a practical scheme that can successfully inter-operate with cellular traffic. The IS-136 design differs slightly from DECT and PACS-UB discussed above in that it is intended to share frequencies in with the cellular operator even though it operates as a cordless technology. Dual cellular/cordless handsets are proposed for the scheme, since frequencies and modulation schemes are common to both scenarios. It is envisaged that when the cellular phone user arrives within the coverage area of the personal base station all calls to and from the handset will be re-routed to the personal base station. The land-line is then used for calls, providing the user with cost savings. If the personal base station is occupied with another user then the cellular phone conversation would continue to use the cellular base station. Particular care has been taken in the development of this scheme to select empty channels to ensure that other cellular phone users remain unaffected. This is done using a novel allocation scheme in which channels are constantly scanned and the level of interference monitored. A cost is then associated with the level of interference on each channel, with the cost values being incremented/decremented each time the channel is scanned. A channel with the lowest cost is selected for use. The cost is also used to determine how frequently the channel should be scanned, with those of the high cost being scanned less frequently. The particular cost values chosen ensure that a channel is only selected if it has been empty for one to two days and given up very quickly (seconds) if interference increases because it has been selected by the cellular operator. The initial frequency allocation is sent to the personal base station over a land-line, which has the added advantage that the allocation can be changed at any time. Relatively detailed propagation and shadowing models have been used in the simulations allowing the authors [12, 13] to specify the number of base stations that could operate per square mile.

2.2 Interference and CIR Based Studies of DDCA There have been several academic studies examining different aspects of DDCA, the ˚ most significant being carried out by three authors, Akerberg, Chuang, and Foschini.

˚ 2.2.1 Akerberg Paper ˚ Some of the earliest work carried out on DDCA was done in 1989 by Akerberg [14] who suggested that an interference threshold be used to determine whether chan-




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nels were free, and could form a coexistence etiquette 1 . It was mentioned earlier that the coexistence etiquette has now been incorporated into a standard by the FCC [7]. This is designed to minimise interference between users sharing the same channel, by specifying a maximum transmission power, and only using channels when ˚ the interference power is less than a given threshold. Akerberg investigates the effects of different interference thresholds on the capacity of the system. A Least Interfered Channel (LIC) channel selection algorithm is used in the absence of power control 2 . A Grade of Service (GOS) parameter is used to assess performance where the probability of call dropping (due to SIR degradation) is weighted 10 times larger than the probability of blocking. Performance is evaluated using several different propagation models for the indoor environment and it is found in all cases that a tighter interference threshold provides the best GOS. This is because in this scenario call dropping dominates when the particular definition of GOS is used. Exactly the opposite conclusion is drawn by Cheng and Chuang [15] when they use a similar protocol, but a different performance measure; they suggest that LIC with no interference threshold performs the best. This is solely a result of how performance is judged, whether call dropping is allowed, and how important signal degradation is ˚ during the lifetime of a call. Akerberg then incorporates a power control algorithm, which allows the signal power to be increased by 1 dB for every dB the interference is below the threshold, in order to cope with localised interference. It is found that the performance of this protocol is worse than that of the non-power control algorithms, and again the performance is highly dependent on protocol and parameters. Chapter 9 of this thesis will show that capacity can be increased by a factor 3 using a similar power control algorithm.

2.2.2 Foschini Paper A paper by Foschini et al (one of the co-authors of the Cimini papers described in the section below) considers a practical DDCA algorithm. The propagation environment is taken more explicitly into account including lognormal shadowing [16]. They consider performance in terms of successful calls, combining together with equal weights call blocking and dropping. Another parameter is also used which provides a measure of a DCA algorithm’s aggressiveness3 , which is defined as the fraction of accepted calls that are dropped. Calls are dropped if their C/I falls below a specific threshold once it is in progress. The main conclusion to draw from this paper is that capacity is controlled by the level of call dropping, with some 95% of failed calls being due to dropping. This is one of the few papers which incorporates a power control scheme into a DDCA model, whilst at the same time examining the effects on a call throughout its lifetime. An iterative power control scheme is implemented which seeks to maintain the CIR around a preset level [17]. If this preset level cannot be maintained the call is either blocked, if it occurs during the set-up phase, or dropped if it is in the in progress phase. No conclusion is drawn as to why 1

This paper forms the basis for much of the work done in this thesis on UFDMA. He suggests that if power control is used it is no longer possible to predict the distance an interfering user is away - this has now been solved by several researchers including the author. 3 This is discussed in more detail in Chapter 3. 2




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the level of call dropping is so high. The only ways suggested of mitigating the problem are to lower the number of accesses on to a channel by increasing the number of channels, or externally limiting the capacity. The reasons why call dropping occurs with DDCA schemes, both with and without power control, is explored in Chapters 7 and 9.

2.2.3 Work with DECT and GSM at the University of Leeds Two papers have originated from the University of Leeds in the UK, looking at DDCA with practical schemes. The first by Saunders et al is loosely based on the GSM frame structure [18]. Two schemes are examined: First Available (FA) and Best Quality. The FA scheme selects the first available channel in a ordered list that meets a CIR threshold 4 . The Best Quality scheme selects the channel with the highest CIR. A brief analytical treatment based on the Erlang B formula is also given, which is very similar to that suggested by Cimini et al [19]. Overall the FA schemes come out best, but call dropping is not allowed; instead call reassignments take place when the CIR degrades below a particular level and a better channel is available for use. It is also important to mention that these schemes do not directly use interference measurements, only CIR. The second paper from Leeds by Law et al extends the work. Now an interference threshold is incorporated into the schemes, with the scheme being loosely based around DECT. In this paper the FA and Least Interfered Channel (LIC) are the assignment schemes chosen. There does however seem to be some confusion in the LIC scheme because the channel with the highest CIR is chosen, rather than the channel with the least interference. This assumes that the signal strength is correlated with the interference (choosing a channel with the highest CIR, ensuring that the channel has the lowest interference), which is unlikely. The blocking performance at various interference thresholds is examined and it is found that this time LIC performs better. No conclusions are drawn as to why LIC performs better in this case.

2.2.4 Chuang Papers Probably the most thorough work carried out into DDCA is that carried by Chuang. He has published several IEEE journal papers over the last 5 years. The first on the subject published back in 1991 [20] developed a frequency assignment process based on local measurements at ports. Once chosen, the frequencies are kept for relatively long periods, unlike most of the other schemes discussed here which choose frequencies on a call-by-call basis. The scheme involves a sense-transmit-sense iteration sequence where the least interfered frequency is chosen during the sensing phase (a TDMA frame is used for transmission). A beacon is then transmitted on 4

This is also referred to by other researchers as Autonomous Reuse Partitioning (ARP) and should not be confused with the FA scheme used in Chapter 4 which chooses the FA channel at randomly from the list.




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this frequency and after a random time the transmitter is switched off and the process repeated. After a few iterations, the same channel is selected each time and the algorithm stabilises, since transmitters are activated and deactivated randomly. This algorithm thereby optimises the frequency reuse distance between transmitters sharing the same frequency (in terms of signal strength not actual distance since shadowing has been included). Performance is compared with random frequency assignment as well as pre-planned frequency assignment and it is shown that the algorithm performs well. Performance is mainly judged in terms of CIR, with CIR cdfs being used throughout. A discussion into the relative benefits of long term frequency assignment against full DDCA is also given along with a correlated shadowing model which can be used to vary the degree of spatial shadowing correlation. He also mentions that with FCA it is possible to get signal outage (similar to dropping but calls remain in progress) due to shadowing when there are call arrivals in cells reusing the frequency. It is found that this scheme is less susceptible to such effects because the frequency reuse distances have been better optimised. Work in Chapter 8 shows that this is also the case with FCA when used in a cellular environment (when the same performance parameters are used as with DDCA). It is interesting to note that PACS-UB developed by Bellcore [1] uses a similar channel assignment process for their ports. The second paper by Chuang in 1993 [21] is a general discussion paper highlighting differences between dynamic frequency planning (his previous paper) and DDCA. In particular, the effects of frequency/time slot scanning are discussed, including how blind spots occur. Blind slots are time slots that cannot be scanned because of the finite time required to switch frequencies, and results presented show this reduces capacity. A DDCA scheme is then investigated which aims to provide a more balanced uplink and downlink SIR by both the port and portable jointly choosing the best channel. This is achieved by firstly the port choosing L channels which have the best SIR on them determined by beacon from each of the ports. These are then transmitted by means of a control channel to the portable. The portable then measures the SIR on the L channels and chooses the best channel. Transmission commences and providing the SIR at the port is above a threshold the call commences, if not up to K retries can be performed. This algorithm is particularly ingenious: if L is 1 then the port effectively chooses the frequency; or if L is 120 (the maximum number of channels in the allocation) then the portable effectively chooses the frequency. In practice L is around 10 with K being around 6. The other advantage of this approach is that the portable only has to scan a few frequencies rather than all 120 as in DECT, which means that the hardware can be less expensive and call set-up times much shorter. Results also show that performance is improved over either the port or portable choosing the frequency alone. It should be noted that this form of DDCA is similar to Japanese and Korean work that uses SIR alone, rather than using the interference level on a channel, coupled with an interference threshold (see Section 2.3). The effects of port timing synchronisation and number of transceivers per port are also investigated. It is shown that the absence of timing synchronisation between ports can considerable reduce capacity. This algorithm is also coupled with an iterative SIR based power control algorithm DAVID GRACE DPHIL THESIS



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similar to that suggested by Foschini in [20]. Once again performance is judged in terms of SIR cdfs with both instantaneous and local mean SIR being used (Rayleigh fading with two-branch diversity is included). It is shown that the power control algorithm is stable and can maintain SIR around a predetermined set level (16dB or 18dB). The SIR statistics are also gathered during the lifetime of calls for every call arrival and departure. It is shown that again the SIR can be maintained around a predetermined level. However, these cdf results do show that a small but finite number of calls have SIR which falls below an acceptable level, caused by arrivals in neighbouring cells. It is shown later in this thesis that the degree of SIR degradation is considerably affected by scenario and choice of acceptable SIR. This is not considered in Chuang’s paper. The fundamental problem with DDCA is that new arrivals can cause SIR degradation. The paper also examines (using an analytical model) the effects that different types of collisions have on performance, resulting from a finite call set-up time. A ‘strictsense collision’ is defined as that occurring when two calls transmit simultaneously - effectively the vulnerable period seen in CSMA, along with ‘wide-sense collisions’ which occur when users in interfering cells set-up simultaneously. Strict sense collisions contribute to higher dropping whereas wide sense collisions contribute to increased interference. Analytical expressions show that the effect of strict sense collisions can be mitigated by allowing retries. The analytical expression for wide-sense collisions seeks to estimate how likely interference will occur. An investigation of capacity with a limited number of transceivers per port is also investigated with performance judged in terms of GOS. GOS is defined as blocking probability plus outage probability (the probability that the SIR at access is below a given threshold). These results indicate that number of transceivers per port is a major constraint on capacity. UFDMA point-to-point (developed later) and cordless technologies are not constrained in this way, but are instead constrained by interference and availability of channels locally, and this is probably one more reason as to why power control works so well in this scenario. A paper by Cheng and Chuang in 1996 [15] examines interference based DDCA schemes with channels determined jointly by port and portable, which also use power control. These are compared with the MAXAVAIL DCA scheme developed by Sivarajan [22], which seeks to maximise the number of channels available to a port based on a constraint matrix. Derivation of the constraint matrix is described in detail in the paper. The matrix takes into account the effects of co-channel interference. Five different DDCA schemes are examined: LIC; LIC with an interference threshold; an algorithm similar to Autonomous Reuse Partitioning (ARP) which seeks to pack the users into the smallest possible frequency, as well as picking the Most Interfered Channel (MIC) below a given threshold; and also a hybrid scheme. Performance is judged by a joint parameter which takes into account the probability of blocking and the probability that the SIR will be below a threshold on either the uplink or downlink at access. It is important to note that the effects of SIR changes during the lifetime of the call are not examined - an important omission, since SIR at access is not directly correlated with SIR throughout the call lifetime. Results show DAVID GRACE DPHIL THESIS



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that overall the LIC algorithm with no threshold performs best and that there is a tradeoff between the blocking probability and probability of poor SIR. The effects of dynamic threshold adjustment are also considered. It is shown that if the best threshold value is used then the performances of all the LIC and hybrid based algorithms are similar, with the MIC based algorithms performing worst. These results are derived for three different propagation scenarios, two outdoors which include shadowing, and one indoors. In all cases, performance is superior to that of the MAXAVAIL DCA algorithm. The MAXAVAIL algorithm was shown to be particularly susceptible to shadowing with a cluster size of 21 being required in order to meet the probability of poor SIR constraint.

2.3 Far East CIR Based Schemes The studies into DDCA from the Japan and Korea have surprisingly originated independently from work published elsewhere in the world. All the papers in this section deal with Autonomous Reuse Partitioning (ARP), Channel Segregation, and/or novel power control schemes applicable for base station oriented schemes, many relating very loosely to the Personal Handy phone System (PHS) designed in Japan. The work stems out of Dynamic Channel Assignment schemes where a cellular frequency reuse plan is modified depending on channel load, rather than using specific measurements of signal strength, interference and/or C/I. Methods of removing the centralised control, typified by such schemes, have almost exclusively adopted a single C/I threshold to determine whether a channel is acceptable for use. No detailed explanation is given as to how the C/I is estimated; instead the simplifying assumption is used that it can be measured in some way. To measure C/I must require the signal to be present at a receiver, either requiring a control channel or probing to be used. The problems associated with probing have been investigated by Foschini [16]. Measurement of C/I obviously represents a disadvantage over the interference-based schemes that only require transmitter activation after channel selection. Often these schemes are compared with FCA with a variety of different cluster sizes, usually 3, 4, or 7. Nodes are assumed to be stationary (or moving slowly) so that the mean signal strength does not change throughout the call lifetime and Rayleigh Fading can be neglected.

2.3.1 Autonomous Reuse Partitioning The benefits of power control in ARP have been specifically examined by Kanai [23]. It is found that power control (maintaining a fixed C/I) provides considerably less increase in capacity than it does with a random channel assignment scheme. Firstly, with the scheme suggested, the power control is done prior to channel selection so that only when power reaches either minimum or maximum does ARP actually take place. He suggests that with ideal power control with infinite range ARP would have no effect because it would be possible to increase power sufficiently to maintain DAVID GRACE DPHIL THESIS



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a fixed CIR. ARP without power control on the other hand seeks to pack channels with similar signal levels onto the same channels. More users with higher signal powers are packed on to the same channel, with fewer users occupying channels if they have lower powers. A scheme based on ARP suggested in [24] implements power control after channel selection, thereby maintaining the allocation order. Both schemes increase capacity by a similar amount compared to ARP with no power control, indicating that the order in which power control and channel assignment is carried out has little effect on system capacity.

2.3.2 Channel Segregation Channel Segregation is a cost based algorithm that prioritises channels according to how successful they have been in the past, in order to limit the number of channels that are likely to have an incoming call. Prior to selection, the CIR is measured on the highest priority channel. A typical cost algorithm is: if the CIR is above an acceptable threshold its priority is incremented and the channel is used. If the CIR on the highest priority channel is below the threshold its priority is decremented and the channel with next highest priority is selected, until a suitable channel is found. Channel segregation schemes have two basic advantages: firstly set-up times are quicker, because in general fewer channels need to be scanned; and secondly, the hardware can be simpler because fewer transceivers are required. Such schemes are used by the IS-136 personal base station described earlier and also in [12, 13]. A detailed study of Channel Segregation is considered in [25] for a TDMA/FDMA structure and is based on a simple cost based algorithm which considers the ratio of number of successful calls over the total number of tries. The higher the ratio the higher is the priority of selection. Carrier sensing is then performed on the highest priority channel, and providing the CIR is greater than a given threshold the channel is used. The performance of the scheme also takes into account the possibility of limited wire-line outgoing trunks. It is found that segregation exhibits superior performance compared with random and fixed channel assignments. The effect of TDMA synchronisation between neighbouring cells is also examined. This is found to have less effect on the channel segregation scheme than it does on a random channel assignment scheme, because the priority algorithm tends to bunch slots from the same carrier together at a particular base station. With the random based scheme the loss of synchronisation tends to lead to more unusable slots owing to overlapping interfering slots from a neighbouring base stations. Slots can also be lost due to frequency switching times. The effects of synchronous/asynchronous base stations are also examined in [26] which considers a DDCA scheme for the Japanese Personal Handy phone System (PHS). They suggest performance is degraded when there are large differences for both synchronous/asynchronous transmission between forward and reverse link EIRP because of interference in a TDD slot. They judge performance by developing a virtual cluster size, which is the number of cells that are needed before all the channels are used. Results are presented which shows that this is dependent on the CIR threshold required: the higher the threshold the larger the cluster size, DAVID GRACE DPHIL THESIS



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causing a lower capacity. A paper by Furuya and Akaiwa [27] looks at channel segregation which allows channels to be loosely assigned to base stations. The scheme uses a simple increase/decrease priority algorithm, whose value depends on whether the channel was last used successfully or found busy. They show that base stations quickly choose their favourite channels, with performance being much better than true FCA since every channel is still available for selection if required at every base station. After convergence, the interference probability (caused by the CIR falling below an acceptable level due to new arrivals on the same channel) decreases dramatically. They also look into the effects of incorporating antenna sectorisation into an already established network operating the scheme. It is shown that the channel assignment quickly adapts to a new pattern. This algorithm looks to be ideal for cellular based schemes, although many would argue that forcing handoffs is a good thing because theoretically it improves capacity. This type of scheme would probably be of limited use where the assigning node, or traffic conditions, changed quickly since the ‘optimum’ channels would quickly become sub-optimum. It may however be possible to alter the rate of convergence or incorporate acceptable CIR/interference thresholds to provide some protection against selecting a ‘wrong’ channel.

2.3.3 Combined Autonomous Reuse Partitioning & Channel Segregation Papers by Park, Okada and Mizuno [28, 29] examined three schemes Flexible Reuse (FRU), Channel Segregation (SEG), and Autonomous Reuse Partitioning (ARP). With FRU the channel with the smallest C/I value above a threshold is selected, with SEG and ARP algorithms similar to those described above. The results presented by Park et al [29] indicate that ARP performs best out of the three schemes tried. Performance is judged in terms of blocking rate and forced termination rate, and can be considered as being similar to blocking and dropping probability. ARP has the lowest forced termination rate and blocking rate for a given level of offered traffic. Performance of the three schemes is also considered with power control included. The scheme seeks to maintain a fixed CIR, although how this is achieved is not explained in detail, but capacity is improved in all cases. The forced termination rate both with and without power control does remain significantly high. A scheme based on Reuse Partitioning by Park et al is considered in [28]. This seeks to estimate the distance between mobile and base station by using the received signal strength. This is then used to determine in which of three concentric rings around the base station the mobile is located. Channels are dedicated to each of the three rings as well as fixed transmitter powers of 0dB, with -6.2dB and -16.7dB for the outer, middle and inner rings respectively, relative to an unspecified reference power. Two channel assignment algorithms are used: the first selects the channels in random order, whilst the second assigns calls into specific channel subsets depending on the ring in which it is located. All channels are used for the inner ring with only the selected better channels being used for the outer ring, determined from their channel number in an DAVID GRACE DPHIL THESIS



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ordered list. It is found that the second algorithm performs best. An alternative channel segregation approach is considered in [30], which is combined with ARP. The channels are divided into subsets and the received signal power is used to determine which channel subset should be used. Once the subset has been selected, the CIR is checked to ensure that it is above the threshold. This approach is compared with FCA and random channel assignment where a channel is chosen at random and tested to see if it meets the CIR threshold. It is found that capacity is very similar to ARP but with the number of channels to be scanned considerably reduced. Both Fixed and Random Channel Assignment performance are worse than ARP. Both the interference ratio (how often the CIR dips below a given threshold) and forced termination probability are used and are comparable in value to the blocking probability.

2.3.4 Power Control Papers by Ishii and Yoshida [31, 32] consider a CIR based DDCA scheme which uses transmitter power control. It is one of the few papers which explains how CIR can be measured - the signal power, presumably originating from a base station, is measured using a beacon on the control channel. They then independently measure the interference power on each of the channels, and thus calculate the CIR available on each channel. In addition, the measurement of the signal power can then be used to connect to the strongest base station. The power control algorithm is effectively integrated into the channel assignment algorithm, because the choice of channel is determined by CIR. The channels with CIR which will benefit most from power control, but at the same time which minimises the impact on other users sharing the same channel, is determined. This is done by adopting a second transmitter power control CIR threshold. This is normally higher than the minimum CIR required on the channel. The value of this threshold can be set differently for base and mobile. The values around this threshold are then divided into four regions:

Region A, both CIRs are greater than the PC threshold; Region B, the CIR of the base is above the PC threshold but the CIR of the mobile is below; Region C, both CIRs are less than PC threshold but greater than the minimum threshold; Region D, the CIR of the mobile is above the threshold but the CIR of the base is below.

The paper explains that most schemes either select channel based on minimum CIR required (ARP), or maximum CIR required, e.g. like DECT. These would correspond to setting the PC threshold equal to the minimum CIR, or setting the threshold as




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large as possible respectively. The results indicate that neither of these options provides the highest capacity. Instead the algorithm picks channels as close as possible to the PC threshold, with available channels in Region A being given highest priority, with Region B next priority, followed by Region C and so on. The power is then adjusted to the PC CIR threshold. Region A is selected first because this causes a reduction in transmitter power of both transmitters, whereas region D requires an increase in the transmitter power of both transmitters. The CIR is increased above the minimum level to provide some protection against forced termination due to new arrivals on the same channel. They consider the performance of this algorithm against ARP- and DECT-like schemes. They show that overall performance is better, exhibiting a lower blocking probability than ARP or DECT, whilst at the same time having a lower forced termination probability. The major drawback of this scheme is that performance is likely to be threshold dependent. If the threshold is set too low, then the scheme turns into an ARP (exhibiting a high forced termination probability) or if set too high then it behaves as a DECT-like scheme with higher blocking probability. Other researchers simulating similar schemes have shown contrary results. For instance Cheng and Chuang [15] simulated a DECT like scheme and suggested that no threshold provided the best capacity. Almost certainly both sets of results will be valid, with slight differences in scenario, performance measure, and algorithm accounting for the radically different conclusions. However, overall the papers are interesting especially as these are the only papers that design a scheme for different required CIRs at base and mobile.

2.3.5 Overview of Japanese Research Hamabe and Furuya [33] provide a general review of DDCA from a Japanese perspective. Work done by European and American researchers on DDCA is not considered. Common performance parameters are outlined such as blocking probability, interference ratio, forced termination probability, and number of channels which are required to be scanned by a base station and mobile before a suitable channel is found. The various common distributed techniques are also discussed such as channel segregation, reuse partitioning, and autonomous reuse partitioning. Add on power control systems are discussed, although practical power control implementation issues are not developed. Results presented show that channel segregation performs best, with ARP performing worst. These contradict fundamentally the work of Park et al, who claim that ARP performs best with Random Channel Assignment (RCA) worst. The main reasons for discrepancy are the performance parameters and simulation set-up. These discrepancies indicate how difficult it is to compare two sets of results, since often it is the specific implementation which causes the improved, or degraded performance. One certainty however, is that channel segregation from an implementation perspective allows fewer channels to be scanned, resulting in faster call set-up times. For such schemes to work successfully they require the channels to be used regularly, so that the priority information used to assign channels is itself up to date. Note that this method of channel segregation differs from that of Jarett et al [12, 13], which alters the priority of the channels based on interference level every time a channel is scanned. The other important conclusion to draw from these DAVID GRACE DPHIL THESIS



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schemes is that they all use call reassignment (intra-cell handoffs) and yet still have reasonably high forced termination rates. It will be shown in Chapters 7 and 9 how forced termination (dropping) can be virtually eliminated without the need for call reassignments. The fundamental problem with performing call reassignments is the rate of Rayleigh Fading, which has been neglected in all of these studies. It could be envisaged that at a particular fade rate any algorithm could be confused into thinking that an intra-cell handoff is needed, when in the long term the call would be no better off on an alternative channel.

2.4 Bounds on the Performance of DDCA Algorithms 2.4.1 Cell based DCA for Single and Multiple Channels Several interesting papers by Cimini et al examined upper and lower bounds on performance of DDCA using an analytical approach. A paper by Cimini and Foschini [34] investigates the maximum capacities that can be achieved in both linear and hexagonal arrays for a single channel (not considering traffic). They also investigate the effects of differing degrees of decentralised control both with, and without, information transfer. This paper is actually a forerunner to a later transactions paper [35] but does contain unique information. They assume a simple channel assignment algorithm is used and a channel can only be selected if it is not being used in the same or a neighbouring cell. This yields a maximum capacity of 33% (cluster size of 3), minimum capacity of 14% (cluster size of 7) with a capacity of 23% if channels are assigned randomly. This is investigated further in later sections. They then investigate the effects of timid and aggressive dynamic channel assignment algorithms, where handoffs are and are not allowed. It is found that handoffs allow the capacity to be increased as the assignment becomes more optimised. The subsequent transactions paper [35], also for a single channel, derived exact expressions for maximum and minimum capacity for the planar array. This was then judged against channels that were assigned at random using the same simple interference algorithm - again a channel could only be used if it was not active in the same or adjacent cells. In the case of the planar array, only a solution for the minimum capacity was available in closed form. It was found that if channels were allocated at random then they could typically support 70% of the maximum capacity at saturation. It was also found that channel usage tended to organise itself into uniform maximally packed blocks as calls arrived and departed. The main conclusion that they draw is that dynamic channel assignment with centralised control provides little extra benefit to overall capacity, with the additional problems of control overhead and latency. Follow-on papers by Cimini et al [36, 19] consider the effects on capacity of trunking efficiency gains obtained by DCA schemes over FCA scheme because they have all DAVID GRACE DPHIL THESIS



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channels available at each base station to select from. Comparisons are made using a novel Erlang B approximation. They assume the same simple interference based reuse algorithm used in the previous papers. They seek to derive a lower bound and typical bound on capacity for a given cluster size. The typical bound is achieved by reducing the number of channels available at each base by a factor Æ , representing the packing loss over and above the theoretical maximum 5 . They then consider the effects of aggressive channel assignment algorithms to see if they yield to improvements in capacity. These lead to call dropping so they suggest that the Erlang B blocking probability now represents probability call unsuccessful, although this is not fully justified. Their results show that DCA performance is better than FCA at low blocking probabilities but then at a particular blocking probability (which decreases as the number of channels are increased) FCA becomes better. However, if the lower bound could be attained (no loss factor) then DCA would always be better off over the whole range of blocking probability. They claim that no matter how much reassignment takes place with this simple reuse algorithm the lower bound will never be reached - which is probably true. If a more efficient channel assignment algorithm were used allowing a flexible cluster size and reuse distance, by using required CIR, then it is likely that the lower bound could be exceeded making DCA better overall. Whilst FCA could take a similar approach through reuse partitioning and/or power control, it is unlikely that that signal levels could be as tightly optimised, especially if lognormal shadowing was present. Overall a very interesting paper, clearly demonstrating that trunking efficiency is an important parameter in determining the capacity of DCA schemes. These papers are significant to the study of DDCA because they effectively decouple detailed propagation behaviour from a dynamic channel assignment model. Obviously, detailed propagation effects such as lognormal shadowing would affect the performance of these models but the important conclusion to draw is that potentially there is little to gain in performance by using centralised control. This is contrary to results presented later in this thesis in Chapter 7.

2.4.2 Interference Based DCA Bounds In a paper by Zander and Eriksson [37] bounds are derived for DCA algorithms based on local interference conditions at base stations. The concept of reuse circles is used which specify a specific D (reuse distance) for a given R (link length). The longer the link length the greater the reuse distance has to be to ensure adequate CIR. The bounds describe the upper and lower limits of the probability of assignment failure, which takes into account both new and calls that require reassignment. It is effectively the probability that a call is unsuccessful. Two bounds are derived, the lower bound (best performance) assumes that it is possible to have continuous reuse distances (not possible with a cellular scenario as reuse distance is set by base station locations), the upper bound assumes discrete reuse distances which could 5

A slightly different approach is taken later in this thesis - instead of reducing the number of channels available for a given cluster size, a virtual cluster size is used over which all channels are available. DAVID GRACE DPHIL THESIS



39

be achieved using (autonomous) reuse partitioning. The model does not explicitly make use of a Poisson call arrival assumption but instead uses the central limit theorem and assumes many nodes. The bounded performance of DCA exceeds the performance of a conventional FCA scheme with a cluster size of 9. Performance graphs are produced in terms of offered traffic per cell per channel - plotting per channel is useful to show the trunking efficiency gains and has been used throughout this thesis. The equations in this paper have been used by several other researchers, e.g. Cimini, and could be useful with different communication architectures such as point-to-point, extensively investigated in this thesis. Although the bounds do not incorporate shadowing effects, the paper provides a significant contribution to understanding. A paper on a similar subject by Whitehead [38] builds on the trunking efficiency work of Cimini and Foschini. He incorporates the effects of interference adaptation into the trunking efficiency equations and provides an excellent introduction into the issues involved. He then goes on to develop a way of estimating the capacity gains of DCA. He assumes that capacity constrained by interference is governed by the median CIR, and that the 10th percentile CIR is the minimum CIR required at a communications node (i.e. this could be classed as having 1% of calls dropped). He looks at the spread in CIR using conventional FCA and assumes that by using DCA with power control this can be reduced almost to zero, thus providing a capacity gain because the median CIR has been reduced. Methods to determine the capacity gains for a typical, and a lower bound approximation, using the Cimini method described above, are then developed. This is tested by simulation of four of the most common CIR based algorithms and the results show that the capacity gain is optimistic. This is put down to the fact (probably correctly) that the maximum capacity cannot be achieved by all units having the same CIR, and that the median CIR was used. The CIR has been used to derive the reuse area, so perhaps the mean CIR should have been used to give the mean reuse area. After all reuse areas caused by high signal powers have significantly more effect on reducing capacity than low signal powers.

2.5 Miscellaneous and General Reviews 2.5.1 General Channel Assignment Review A comprehensive survey of all aspects of channel assignment for wireless communications, with some 98 references, has been carried out by Katzela and Naghshineh [39]. Firstly, FCA is discussed, along with channel borrowing schemes that seek to improve capacity when there is uneven loading. The review then goes on to cover DCA, both in centralised and distributed forms, including both signal based and cell based distributed schemes. Centralised schemes most often work with a matrix of constraints which determine how often the frequency can be reused, and rarely




40

use signal information 6 . Signal based schemes normally operate with base stations fully autonomous of one another, whereas, cell based schemes obtain control information from neighbouring cells about the channels which are in use. The paper then draws comparisons between FCA and DCA schemes. They suggest that DCA is better at low traffic loading with FCA better at high traffic loads. This is contrary to that shown in several other papers which use signal based channel allocation (see above). Later in this thesis analysis will show that the relative performance will depend on the virtual cluster size created by the DCA scheme. Hybrid channel assignment schemes are discussed where some channels are preallocated as FCA with others left for DCA. It is shown that these improve capacity over the corresponding separate FCA and DCA schemes. Flexible allocation schemes are then considered, in which channels are allocated to a base station on a long term basis from a central pool (rather than on a call by call basis in the case of centralised DCA). It is shown that this yields better performance than FCA without the signalling overhead created by the centralised DCA schemes. Handoff strategies are then discussed and it is explained that many of the schemes do not consider them, making performance unduly pessimistic. Guard channels are sometimes used with the handoff schemes, which are kept aside solely for the use of handoffs, thereby ensuring fewer call terminations. Some handoff strategies also aim to reduce both call blocking and forced terminations by allowing newly initiated and handoff calls to be queued until a suitable channel becomes available. Later in the review Reuse Partitioning (RUP) is outlined along with the distributed version, Autonomous Reuse Partitioning (ARP). Both schemes can be used to increase capacity. Finally, Power Control schemes are briefly examined along with other miscellaneous channel assignment schemes. Overall, this paper is well worth reading if a broad overview of the different channel assignment methods is required along with their resulting implications. It should be noted that the review does not cover the full range of DDCA schemes covered here, but that apart, it does put DDCA into context of wireless channel assignment schemes.

2.5.2 Market Driven Implications for DDCA The advantages of DDCA for multiple operator market (rather than regulator) driven communications is considered in [2]. It is explained that if multiple operators use a FCA scheme, causing the spectrum allocation to split into blocks that contain relatively few channels then this can lead to trunking inefficiency. This is not a problem if DDCA is used since the full number of channels is available to all users. How6

This means that the mean cluster size is either equal to or worse than the corresponding FCA cluster size. Signal level based schemes can yield mean cluster sizes that may be lower than the corresponding FCA cluster size. When FCA is used, the cluster size is normally set for worst case conditions (setting the CIR), normally on the edge of the cell. When signal level DCA schemes are used, it is effectively the mean signal level which sets the mean virtual cluster size, mobiles close to the base station having a lower cluster size than mobiles on the edge of the cell. DAVID GRACE DPHIL THESIS



41

ever, disadvantages are apparent: including creating a suitable incentive scheme to encourage operators to invest in infrastructure. This problem arises because if an operator increases the base station density to shorten link lengths thereby creating extra capacity for itself, then there are also capacity advantages for other operators through lower interference. The paper outlines possible solutions, including limiting the transmit power, and limiting the number of transceivers per base station. This ensures that base stations are capacity rather than range limited. At the end of the paper, a DDCA scheme employing ARP is simulated with 4 operators having different market shares. Handoffs are not simulated; instead, it is assumed that spare channels will be allocated solely for the use of handoffs. They do, however, note that at 1% blocking probability between 17% and 33% of calls have interference below an acceptable threshold. They also indicate that even the best handoff schemes only ensure that 99% of handoffs are successful, implying that calls will be dropped. However, overall they consider this ‘a price well worth paying’ for the advantages of allowing innovation and competition. In several chapters of this thesis ways of mitigating call dropping are discussed, some of which would be suitable for cellular situations. This paper highlights the important issues which need to be addressed if multiple operators (>3) are to coexist successfully in a given allocation, whether using FCA or DDCA.



Chapter 3 Simulation and Verification Methodology Contents 3.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.2

Modelling of Communications Architectures . . . . . . . . . . . . . 43

3.3

Simulation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.4

Introduction to the Pictorial Model . . . . . . . . . . . . . . . . . . . 52

3.5

Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.6

Validation using Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.7

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

3.1 Introduction The aim of this chapter is to provide an introduction into the DDCA evaluation methods used throughout this thesis. Much of the research relies heavily on simulation. Wherever possible results are supported by alternative methods, such as analysis and a novel pictorial model. Considerable time has been spent developing the pictorial model which has been used to explain the behaviour of DDCA and also as a development tool for the design of improved DDCA schemes. Simulation has been adopted as the main method of evaluation for three main reasons. Firstly, computing power today is sufficient to accurately model the behaviour of systems in which complex interactions take place. Secondly, analytical techniques for modelling such complex systems are not available, and the main intention of this study was to develop simulation techniques which would enable a wide understanding to be gained into the behaviour of DDCA models in realistic environments, rather than develop novel mathematical descriptions of their behaviour for simplified scenarios. Thirdly, practical trials, one other possible solution to gain understanding DAVID GRACE DPHIL THESIS


42

CHAPTER 3. SIMULATION AND VERIFICATION METHODOLOGY

43

into the behaviour of DDCA, were discounted owing to the level of financial support and development time required. In addition they would be unlikely to provide any improvement in understanding the basic tradeoffs when compared with well developed simulations. Two simulation tools have been used, OPNET for modelling protocol behaviour, and MATLAB for modelling algorithmic behaviour. Firstly, a general overview of how DDCA schemes have been modelled is presented. This is followed by a descriptions of how models are developed within the OPNET, and how DDCA simulations are developed in MATLAB. The basic principles of the pictorial model are presented next, prior to a more detailed treatment in subsequent chapters. A discussion of how performance of DDCA schemes should be evaluated is then covered. Finally, possible analytical approaches are discussed, such as the Engset Distribution and Erlang B formula, to provide a performance comparison for simplified DDCA schemes.

3.2 Modelling of Communications Architectures To effectively model a communications architecture it is necessary to take into account both the geographical and temporal effects. Three architectures are considered in this thesis:

point-to-point, where pairs of nodes communicate together; point-to-multipoint, where one user communicates simultaneously to other users, and in thesis an all-informed net is considered; cellular, where nodes communicate with each other via central base stations.

Each architecture has been modelled separately, with many nodes dispersed over a geographical area so that the effects of frequency reuse can be taken into account. As mentioned previously in Chapter 2, the DDCA schemes developed in this thesis are based around Frequency Division Multiple Access (FDMA). However, rather than a central controller determining the channel assignment, it is carried out locally, i.e. DDCA is performed. For this reason the schemes are collectively referred to as Unsupervised FDMA (UFDMA). To determine whether a channel is occupied the interference level at the transceiver site is measured and if the interference is above a set interference threshold, then the channel is considered occupied. For a call to be successfully set-up then the Signal to interference plus Noise Ratio (SNR) must be above a set threshold MINSNR. Description of the various UFDMA schemes is provided in later chapters. The important feature to consider is that multiple calls will be taking place on each of the frequency channels. When modelling the overall system it is therefore necessary to ensure that at any time instant the operation of all the relevant nodes is taken into account. DAVID GRACE DPHIL THESIS



44

3.3 Simulation Techniques Two tools have been used to determine the performance of UFDMA: OPNET and MATLAB. Both have been used to perform Monte Carlo simulation, where the performance over a large number of trials is assessed and the statistical performance determined. Simulations were developed first in OPNET, a commercial network protocol development tool. A UFDMA protocol was developed, and communication down to the packet level was simulated. This allowed temporal effects such as call set-up time at an individual node to be accurately modelled, but the simulation complexity prevented system wide behaviour from being assessed. Later on UFDMA access schemes were developed in MATLAB. This enabled just the bare essentials to be captured, namely the effects of call arrivals and departures, rather than the detailed packet behaviour. Such simulations allowed the system level to be modelled. More recently (and used to verify certain UFDMA IA in Chapter 7) it has been shown how OPNET can also be used to model the bare essentials of the scheme. This highly unorthodox approach does not use the packet feature of the tool.

3.3.1 UFDMA Simulation Using OPNET OPNET is designed to model network characteristics and when used conventionally assumes that data are to be transferred in packets. Both fixed wire-based and mobile radio networks can be modelled. OPNET allows the time domain characteristics to be explored in detail, and geographical location dependencies, which at first seemed difficult to model, can be explored by dynamically specifying the locations of mobile nodes according an appropriate random distribution. OPNET has proved useful in obtaining call set-up times along with assessing the robustness of UFDMA to interference and spoofing. OPNET allows models to be built in a hierarchical fashion. The highest layer, called Network Editor, allows communication nodes to be placed on a geographical layout. These nodes to have to be placed by the developer prior to simulation but as mentioned above mobile nodes are later placed at a random location during the simulation. A typical view is shown in Figure 3.1(a). The next level of the hierarchy is the Node Editor as shown in Figure 3.1(b). This allows a block diagram of individual communications nodes to be constructed. Typically this consists of processing blocks (e.g. ‘simulation’), packet generators, queues, transmitters and receivers. These are linked together by ‘wires’. Stream wires allow data packets to be transferred between blocks (shown as solid lines). Statistic wires allow discrete statistics to be transferred between blocks (shown as dashed lines). The lowest level of the hierarchy is the Process Editor, which allows the functions DAVID GRACE DPHIL THESIS



45

(a) Network Level

(b) Node Level

(c) Process Level

Figure 3.1: A typical OPNET model, illustrating the three editor views DAVID GRACE DPHIL THESIS



46

of the processing blocks of the Node Editor to be defined. Defining the processing blocks takes most of the time when developing a new scheme. Processing blocks are defined using a language called Proto C which is a graphical extension of the ‘C’ computer language. Proto C uses states, similar to those used in a finite state machine (FSM). Most functions of networks can be represented in FSM form. States, and transitions between states, are set up graphically. A typical process model is shown in Figure 3.1(c). Only one state within each processing module is active at one time and the program commences in the ‘init’ state at the start of the simulation. Each state can either be forced (shown in black in this thesis) or unforced (shown in white). Transitions to and from states occur through interrupts, with the specific state transition controlled by the conditions shown on the arrows between the states. Each state has entry and exit conditions. With a forced state entry and exit conditions are executed sequentially with no pause between entry and exit conditions, and are usually used to compartmentalise the code into a visual block. With unforced states there is a pause between execution of entry and exit conditions, whilst the process waits for the next interrupt. Interrupts are the main way in which flow control is implemented in OPNET. Forced states can therefore be considered as waiting states. The entry and exit conditions of each state are then specified using conventional ‘C’. OPNET also has numerous ‘Kernel Procedures’ which are ‘C’ functions to perform most of the tasks required in the processing block. For instance they control packet movement both into and out of a block. To develop a particular processing block the developer has to combine together the appropriate kernel procedures in a ‘C’ code program for each state in the processing block. Each processing block then operates in parallel at simulation time to simulate a real network, in which each processor in a node operates independently. The parallel behaviour is achieved by time stamping each event with the appropriate simulation time so that all events with the same simulation time are taken into account.

UFDMA Protocol Implemented in OPNET A call protocol has been developed on OPNET for use with UFDMA[40]. A call consists of a forward and reverse link occupying different frequency channels. The call itself is divided into three main phases: Set Up, In Progress, and Clear Down, with two additional related phases Dropped and Blocked. The three main phases have been adopted so as to define when a call can be Dropped or Blocked. A Blocked call can only occur when either Caller (CR) or Callee (CE) node is the Set Up phase and Dropped calls can only occur when both nodes are in the In Progress phase. A call starts its life in the Set Up phase with a call commencing at the CR node. Firstly, a forward link between CR and CE node is established, with Set Up packets sent along the link. On receiving an incoming call from a CR node the CE node tries to generate a reverse link on a separate channel, by sending Set Up packets back to the CR node. If these packets are received, the call state changes and In Progress packets are sent along forward and reverse links. If the call is not setup within a given time limit then the call is blocked.




47

A call remains in progress until it is either dropped due to interference, or cleared down from the CR node end. To clear down a call the CR node sends out Clear Down packets. On receiving the Clear Down packets the CE node acknowledges them by sending Clear Down packets back to the CR node. The CR node ceases transmission as soon as it receives a Clear Down packet. The CE node terminates transmission after a packet time out occurs. The various call phase transitions are shown in Figures 3.2-3.4. Time is shown vertically with each vertical bar representing either the CR or CE node. The various packet transmissions are shown by arrows with the call phase shown above. The two digits (0 or 1) after the call phase label represents the state of the forward and reverse link acknowledgement bits respectively (0 represents FALSE, 1 represents TRUE).

3.3.2 UFDMA Simulation Using MATLAB MATLAB uses an interpreted language looking similar to ‘C’ to describe behaviour. MATLAB was originally developed as a mathematical tool to solve matrix equations and problems in control theory. Matrix equations can be formed and solved incredibly easily, without the need to generate external loops, as would be necessary in ‘C’ to operate on the matrix (array) elements. However, there is a price to be paid in terms of execution speed; interpreted MATLAB code typically runs 10 times slower than compiled ‘C’ code. Conditional statements and user defined loops run particularly slowly. It was decided that it was far more important to develop simulations quickly, and allow easy modification, rather than to have them execute quickly. Simulations could be run overnight or over several days without user intervention, whereas long development times occupy the user for the whole of the time1 . MATLAB has been used to capture the main features of the DDCA, such as call arrivals, departures and interruptions, rather than describing calls on a packet by packet basis, as is done in OPNET. By modelling only the discrete events simulation times can be dramatically increased without the loss of any really useful information. A typical UFDMA simulation ‘engine’ is shown in Figure 3.5. The numbers given in brackets are the typical number of elements contained in the matrices. MATLAB is very good at modelling tasks that can be vectorised, with a vector (one dimensional matrix) being filled in a single operation. Performance degrades severely if matrices or vectors have being filled element by element. For this reason the engine relies on most of the calculations, such as path loss, call arrival and departure times to be determined at the start of each run, thereby making the best use of MATLAB’s matrix and vector operations. Computation time is also saved as the path loss and shadowing matrices are reused many times throughout a simulation run, since the values remain constant owing to nodes remaining fixed. The main drawback of MATLAB compared to OPNET is that code is run sequentially rather than time 1

MATLAB does have an add-on compiler which should improve performance, although this has not been used here.




CR

48

CE Setup 0 0 Setup 0 0 Forward Link

Setup 0 0

CALL SET UP PHASE

Reverse Link Acquisition

Acquisition

Setup 0 0

Setup 1 0

Setup 0 0

Setup 1 0

Setup 0 0

Setup 1 0 Setup 1 0

In Progress 0 1 CALL

In Progress 1 0

IN PROGRESS

Immediate change of Call Status

PHASE

(a) Normal Setup

CR

CALL IN PROGRESS PHASE

CE

In Progress 1 0

In Progress 0 1

Clear Down 1 0

In Progress 0 1

Clear Down 1 0

Clear Down 0 1 Clear Down 0 0

CALL CLEAR DOWN PHASE

Clear Down 0 0

Packet Ack T_out Link Terminated

(b) Clear Down

Figure 3.2: Normal Setup and Clear Down phase transitions DAVID GRACE DPHIL THESIS



Call Block T_out

CR

49

CE Setup 0 0

Channel Block T_out

CALL SETUP PHASE

Setup 0 0

Reset TX

Setup 0 0

Channel Block T_out

Setup 0 0 Setup 0 0

Reset TX Clear Down Call

CALL BLOCK PHASE

(a) Block Call at CR Node

CR

CE Setup 0 0

Call Block T_out CALL SETUP PHASE

Setup 0 0 Setup 0 0

Setup 1 0

Setup 0 0

Setup 1 0 Setup 1 0

CALL BLOCK PHASE

Setup 1 0

Channel Block T_out Reset TX Call T_out Link Terminated

(b) Block Call at CE Node

Figure 3.3: Blocked Call phase transitions




CR CALL IN PROGRESS PHASE

50

CE

In Progress 1 0

In Progress 0 1

In Progress 1 0

In Progress 0 1

Packets from CE

In Progress 1 0

no longer received

Call

at CR

In Progress 0 0

T_out

Dropped 0 0 Dropped T_out

Dropped 0 0

CE node reset

CALL DROP PHASE

(a) Drop Call at CR Node

CR CALL IN PROGRESS PHASE Call T_out

CE

In Progress 1 0

In Progress 0 1

In Progress 1 0

In Progress 0 1 In Progress 0 1

Packet Ack T_out

Call T_out

In Progress 0 0

Packet Ack T_out

Dropped 0 0

Reset TX

Reset TX Dropped 0 0 Link Terminated CALL DROP PHASE

(b) Drop Call at CE Node

Figure 3.4: Dropped Call phase transitions DAVID GRACE DPHIL THESIS



51

START

STOP

Generate statistics

DETERMINE:

YES

Node locations 2x(200,1)

Simulation

NO

Path loss matrix (200,200) Shadowing matrix (200,200)

finished ?

Call arrival times (6000,1) Call departure times (6000,1)

Increment iteration number Sort arrival times

YES

Run finished ?

NO

Select next arrival time

Choose channel Increment run number

Determine calls on same channel

NO Block call

Can call be accepted ? YES

Vary power (if implemented)

Check for, and drop calls

Figure 3.5: A typical MATLAB UFDMA simulation ‘engine’.




52

stamped to effectively execute in parallel as it is does in OPNET. Special care has to be taken when configuring the simulation so that events which should occur simultaneously are taken into account when performing the appropriate interference calculations. This also restricts the type of scenario that can be modelled. The code needed in the ‘Determine calls on the same channel’ block even in the most basic models (such as non persistent schemes) where calls are blocked and return at a predefined later time, can be quite complex. Modelling of 1-persistent schemes, where potentially a blocked call keeps retrying, are much more difficult to model since they requiring constant updating of the single element representing a call arrival time, as well as adjustment of future call arrival times.

3.4 Introduction to the Pictorial Model A novel pictorial model has been used to describe the behaviour of DDCA. Pictorial representations of the spatial behaviour of multiple access schemes have been used by several researchers [41, 42, 43] to model both the space and time effects, with one dimension representing time (e.g. due to propagation delay) and the other representing distance separating the nodes. The closest model to that used to describe UFDMA was originally developed by Abramson [44] and then used by Takagi, Kleinrock and Silvester to suggest the optimum transmission ranges for a multihop packet radio network [45, 46], and by Tobagi and Kleinrock to describe the hidden terminal problem in CSMA [47] which has very recently (Oct 1997) been modified by Zahedi and Pahlavan [48]. These pictorial representations differ from the other space-time techniques in that now both horizontal spatial dimensions are used. When nodes are operated in a non-shadowing scenario then this results in a circular contour of signal strength (interference) or an exclusion area which can be drawn around any active transmitter. The early analysis by Abramson has been extended for call oriented UFDMA in that both an Interference Exclusion Area (IEA) and SNR Exclusion Area (SEA) have been used to model behaviour.

3.4.1 Basic Concepts of the Pictorial Model The size of the IEA is ultimately set by the interference threshold. If the node is active shown at the centre of the IEA in Figure 3.6(a), then new nodes situated outside the IEA will measure interference below the threshold, allowing the same frequency channel to be used for a new transmission. If new nodes are located within the circle then they will measure interference above the threshold meaning that the node will be prevented from transmitting on that channel (by the rules of the algorithm and will be blocked if no other channels are suitable) - effectively defining an area of exclusion. A new node located on the edge of the IEA will measure interference at the interference threshold. Figure 3.6 shows the effect of adopting tight or loose interference thresholds. A tight interference threshold means that a node can tolerate less interference before a new transmitter is prevented from being activated on the same




53

New TX

New TX

TX

RX

TX

Interference Exclusion Area (a) Loose Interference Threshold

RX

Interference Exclusion Area (b) Tight Interference Threshold

Figure 3.6: The effect of tight and loose interference threshold on size of interference exclusion area. channel. Therefore the exclusion area is larger for a tight interference threshold than for a loose interference threshold. Clearly, using this approach if all transmitter sites obey the interference thresholds then the frequency reuse distance, and ultimately grade of service, can be controlled indirectly. The effect of the noise floor 2 is to increase the size of the exclusion area when compared with an ideal system in the absence of noise. This can clearly be seen when the noise floor is at the same level as the interference threshold; in this situation no frequency reuse can take place because any node activated on the same frequency will result in the interference threshold being exceeded. This means that in practice the size of exclusion area is governed by both thermal noise and interference due to frequency reuse. The SEA is more complex to understand. The SEA is centred on the receiver and for a call to be successfully received with SNR above MINSNR, then transmitter(s) reusing the frequency must be located outside the SEA. That is, the SEA defines an area of exclusion in which transmitters must not reuse the frequency, otherwise the call will fail due to inadequate SNR. The size of the SEA is determined by the value of the MINSNR threshold, received signal strength, interference and noise floor. Obviously those nodes situated just on the edge will result in the SNR being equal to MINSNR. The way in which a call fails depends on which state the call is in when the frequency is reused in the SEA. If a call is in the set-up phase and the SEA is occupied prior to activation, then the call is blocked. If the call is in progress and a new transmitter is 2

The noise floor is assumed to be caused mainly by thermal noise. Throughout this thesis a noise floor of -130dBm has been used corresponding to a noise temperature of 300K and bandwidth of 25kHz. A noise floor of -130dBm means that any signal received below -130dBm will not be detected using conventional receivers. DAVID GRACE DPHIL THESIS



54

activated in the SEA then the call is dropped. Figure 3.7 shows the effect of a new transmitter sharing the same frequency situated on the edge of the exclusion area for different link lengths. Assuming a particular fixed noise floor, then a long link requires a larger exclusion area than a shorter link because the wanted receiver signal power is less, to ensure that the MINSNR threshold is maintained.

New TX

TX

New TX

RX

TX

RX

SNR Exclusion Area SNR Exclusion Area

(a) Long Link Length

(b) Short Link Length

Figure 3.7: The Effect of Link Length on SNR Exclusion Area Consider a second example: the situation when the signal strength at a receiver is equal to the noise floor, with MINSNR of 0dB. In this situation the radius of the exclusion area would have to be infinite, i.e. no frequency reuse can take place. The situation with both the interference and SNR exclusion areas is further complicated because the above examples assumed a single node would try to share the common frequency. If multiple nodes share a common frequency then the number of nodes surrounding the link of interest would affect the radius of the circle. The more nodes surrounding the link the larger the radius of the circle has to be; however, the basic concept still applies. This problem is investigated in more detail in Chapter 9. Figure 3.8 illustrates the combined effect of using both the IEA and SEA in a frequency reuse environment. Figure 3.8(a) indicates a shaded area called the ‘vulnerable region’ 3 . New transmitters can be activated within this region because the interference threshold provides insufficient protection, and as a result transmitters activated in this area during the life time of a call from the link shown will result in excessive interference so that the call will drop. With the simplex link shown (or frequency division duplex link) call dropping can only be controlled through the use of the interference exclusion area. If the interference threshold is tightened then the 3

Similar behaviour is found in the behaviour of CSMA and is often referred to as the hidden terminal problem, where two transmitting nodes cannot detect each other but their signals can both be received (not simultaneously) at a common receiver [47]. This is described in more detail in Chapter 6.




TX

55

RX

SNR Exclusion Area

Vulnerable Region

Interference Exclusion Area

(a) Illustration of interference and SNR Exclusion Areas

TX

RX

SNR Exclusion Areas

Interference Exclusion Areas

Vulnerable Region

(b) Effect of tightening interference and SNR Thresholds

Figure 3.8: Pictorial model for a single UFDMA link




56

interference exclusion area can be increased so it completely covers the ‘vulnerable region’ as shown in Figure 3.8(b). However if a higher value of MINSNR is required then this may once again mean that a new vulnerable region could be created. It can be seen that this method of protecting MINSNR results in a much larger area being excluded than is required to protect the MINSNR threshold. A scheme which alleviates this problem is presented in Chapter 7.

3.5 Performance Measures Considerable effort has been spent on selecting suitable performance measures with which to assess UFDMA. Typically, data-oriented schemes tend to measure performance in terms of throughput and delay [49, 50], whereas speech-oriented schemes (especially wire-based telephony) often use probability of blocking against offered traffic [51, 52]. Mobile telephony also needs to consider a further measure, namely the probability that the call will be dropped, since dropped calls can occur considerably more often than with wire-based telephony [14]. Sometimes the probability of outage (how likely is the SNR fall to below a fixed value) [50, 21, 15] is used instead. ˚ Other researchers such as Akerberg[14] have used other definitions such as a Quality of Service (QOS) parameter which weights a dropped call as 10 times worse than a blocked call. Cheng and Chuang [15] also use a Grade of Service parameter, but instead equally weight a blocked call (no channels being available) with a link which is below a given SNR. Calls are not dropped in this scenario. The traffic load t is specified in two ways in this thesis, either in calls per channel or Erlangs per channel. The ‘channel’ over which the traffic is measured refers to the bandwidth occupied by a UFDMA frequency channel.

3.5.1 Probability of Call Blocking Blocking probability has been used to measure the probability that a call will fail during the set-up phase. The probability of call blocking at a traffic load t is defined as

B (t) =

Number of call blocked at load t Total number of successful set up calls plus blocked calls at load t

(3.1)

Blocking probability at a particular level can be used to determine the capacity of a scheme, i.e. how many active or potentially active users the system can support. Subsection 3.5.6 discusses two ways in which traffic load can be measured. The capacity of the scheme which is achieved at a maximum blocking probability of Bc can be defined as




CB = t(Bc )

57

(3.2)

3.5.2 Probability of Call Dropping The call dropping probability can be used to provide a measure of performance for a call that fails when it is in progress. The probability of call dropping at traffic load t is defined as

D(t) =

Number of call dropped at load t Total number of successfully completed calls plus dropped calls at load t (3.3)

Again a capacity measure can be defined, with the sole constraint being the maximum drop call probability. The capacity of the scheme achieved at a maximum drop call probability of Dc can be defined as

CD = t(Dc )

(3.4)

3.5.3 Probability of Call Unsuccessful Assessing performance using blocking or drop call probability alone can give a misleading picture of the performance, as there is an inherent tradeoff between call blocking and call dropping in wireless communications, allowing greater frequency reuse [53]. Using the probability that the call is unsuccessful takes into account both call blocking and call dropping and can be used as a parameter in determining the capacity of the system or channel [16]. This parameter weights both call blocking and dropping equally, answering possibly the only really important question to the end user, ‘How often are you going to get a bad call ?’. The end user on a civilian cellular network would like to get through every call, and for it to complete successfully. The probability that a call is unsuccessful for a traffic load t is defined as

U (t) = 1

[1

B (t)][1 D(t)]

(3.5)

where B is the probability that the call will be blocked, and D is the probability that the call will be dropped. Once again another definition of capacity can be defined; for a maximum probability of call unsuccessful Uc the capacity can be expressed as

CU DAVID GRACE DPHIL THESIS

= t(Uc)

(3.6)



58

3.5.4 Call Aggressiveness Call Aggressiveness is a second parameter suggested by Foschini et al [16] and again takes into account the effects of both dropping and blocking. Call aggressiveness can be considered as the fraction of the unsuccessful calls which are dropped due to new calls being accepted. This has been defined for this work, for a particular traffic load t as

A(t) =

D(t) B (t) + D(t)

(3.7)

where once again B is the probability that the call will be blocked, and D is the probability that the call will dropped. For example if a system has a blocking probability of 10% and a dropping probability of 10%, then it would have an aggressiveness of 50%, i.e. 50% of all calls fail due to dropping. An aggressiveness parameter is useful due to the inherent blocking probability / dropping probability tradeoff which occurs for all multiple access algorithms. It is possible for different systems to have identical probability of call unsuccessful behaviour (important to the end user) but considerably different aggressiveness behaviour. If aggressiveness is considered as being the overriding constraint, the capacity for a maximum aggressiveness Ac can be defined as

CA = t(Ac )

(3.8)

3.5.5 Grade of Service A Grade Of Service (GOS) has been used to determine the capacity of some of the UFDMA schemes. This has been designed to take account of the fact that users rate call dropping as being considerably more annoying than call blocking. For instance ˚ some other researchers such as Akerberg [14] have suggested a suggested a Quality of Service (QOS) parameter which he considered rates a drop call as being 10 times worse than a blocked call [14]. Mathematically

QOS = B (t) + 10:D(t)

(3.9)

However, throughout this thesis the GOS has been set in terms of call blocking and dropping, such that the blocking probability never exceeds 5% and dropping probDAVID GRACE DPHIL THESIS



59

ability never exceeds 0.5%4. Definitions of capacity of a scheme can be determined from blocking probability and dropping probability versus traffic load (see above). The minimum capacity determined by either the blocking or dropping probability threshold is taken as the GOS capacity. For examples if a UFDMA scheme crosses the 5% blocking threshold at 3 E/channel and the 0.5% dropping threshold at 2 E/channel then the capacity of the scheme is taken as 2 E/channel. Mathematically the capacity, CGOS is given by

CGOS = min(t(Bc ); t(Dc ))

( )

(3.10)

where t B is the offered traffic which causes the blocking probability to equal the threshold (5%), and t D is the offered traffic which causes dropping equal to the threshold.

( )

3.5.6 Demanded Traffic and Offered Traffic Traffic can be measured in several different ways and the two used in this thesis: demanded traffic and offered traffic are explained below. Both can be used in the above equations as a traffic load t. Demanded traffic is an instantaneous measure of the number of calls which are either in progress, or wish to be in progress at any time instant. Demanded traffic represents the traffic at a particular snapshot in time, i.e. if a line is occupied then it has 1 call on the line. Consider a 10 line system, this would support 10 calls with no blocking, but with 11 calls 1 call would always be blocked. This would give rise to 0 blocking probability when the demand is 0-10 calls in the system but have a blocking probability of 1 when the demanded traffic is 11 calls, because the new call would always fail when the traffic load exceeded 10 calls. One advantage of this parameter is that it effectively separates out the trunking effects present in the offered traffic results allowing geographical reuse effects to be modelled separately5 . The disadvantage of using demanded traffic is that it is difficult to compare performance with other techniques which use offered traffic, since the offered traffic level is dependent on the mean level of demanded traffic. The distribution of demanded traffic is not known in the work covered in Chapter 4 which makes this mean level difficult to obtain. For this reason this technique is only used in Chapter 4, other chapters use the more conventional offered traffic. Relative performance of the schemes using the same traffic measure will be valid, although the absolute improvement of one scheme over another will depend on the traffic measure used. 4

This also means that the maximum aggressiveness can be 9.1%. Statistical multiplexing effects, where the probability of blocking decreases for the same level of demanded traffic per channel will now occur, because the number of channels available at a location will be dependent on interference from neighbouring nodes. 5




60

Offered traffic is an average measure of traffic on a line or system, i.e. if a line is used on average 50% of the time then the offered traffic is 0.5 E. For a finite number of nodes M offered traffic can be defined in terms of node activity factor 0 or traffic intensity parameter as6

G=M

0

1 + 0 =

(3.11)

The corresponding blocking performance of the example when measured in terms of offered traffic would be quite different. Again blocking would occur when more than 10 calls were instantaneously present in the system, but the probability that more than 10 calls were in the system is dependent on the offered traffic load. The probability that there were more than 10 calls in the system would be higher when there was 8E of traffic compared with 1E of traffic. An offered traffic load of 10E means that 50% of the time the number of calls exceeds 10, and 50% of the time the number of calls7 is below 10 . Obviously blocking only occurs when the number of calls exceeds 10. In the case of offered traffic, 1 Erlang is defined as being a two way (duplex) link which is permanently in use. Therefore in the case of UFDMA point-to-point links, each forward and reverse channel, if fully occupied, will each support 0.5 Erlang. In the case of demanded traffic 1 channel will support 0.5 of a call in the absence of frequency reuse. The definitions can be extended for an all-informed net (point-to-multipoint). If the definition is taken to mean the amount of traffic flowing on links then it is possible to consider a half duplex conversation on a point to multipoint link to have 0.5 Erlang of traffic flowing in each direction, which is identical to a half duplex point-to-point link. Therefore offered traffic for a point-to-point link system can be considered as being the same as on a point-to-multipoint system. Considering it from another direction, if a point-to-point conversation is overheard by other links (i.e. pointto-multipoint) then this cannot be considered as new traffic. A full duplex pointto-multipoint system implemented in UFDMA however would create more traffic because more channels would be required to maintain full connectivity. The traffic load has been normalised in respect of the number of channels to allow convenient scaling, and also to illustrate that the capacity per channel is dependent on number of channels in the allocation [15]. Capacity per channel is often in the classic queueing formulae such as the Engset distribution and Erlang B formula, where the blocking probability is shown against offered traffic per channel, with number of channels as a parameter [54, 52]. The Erlang B formula is used to generate blocking probability against offered traffic of such a system if there is an infinitely large pool of users. This relationship will be discussed at greater length in later in this chapter. 6 These terms will be expressed in terms of the arrival rate and departure rate in Subsections 3.6.1 and 8.2.2. 7 This assumes that the median is equal to the mean, which is true assuming the sources are Poisson.




61

3.5.7 Error Bars Error bars have been plotted on some graphs in this thesis, where results have been obtained from a known small number of trials. Two types of confidence limit are plotted, firstly an error in the sampled mean; this type of error bar can be used in plots of throughput versus offered traffic, where throughput can take any value between 0-100%. The size of error bar is determined from [55]

e = zc : p N

(3.12)

where is the sample mean, the standard deviation, N number of trials, and zc relates to the confidence interval. For N > the confidence interval is taken from the normal distribution. For a 99% confidence interval corresponding to zc : , these error bars are always the same size above and below the sample mean. For N , zc should be replaced by the t-distribution, with N degrees of freedom.

30

= 2 56

30

1

The second type of confidence limits occur when events can take discrete rather than continuous values. Discrete binary events are considered here, such as call blocking or dropping. Either a call can be blocked or not blocked, dropped or not dropped. Therefore, to evaluate error bars for blocking and dropping probability, the theory of confidence limits on binomial populations [55] can be used. The size of the error bar is related once again to the number of trials, but also this time to the probability the event occurs. For a large number of trials N , with replacement from a finite population, the size of error bar is given by [55]

e = p zc :

r

p(1 p) N

(3.13)

where p is the probability of the event occurring and zc is the confidence limit given by a normal distribution. For a smaller number of trials the more exact expression is [55]

e=

p+

zc2 2N

zc

q

1+

p(1 p) N zc2 N

+

zc2 N2

4

(3.14)

This final expression yields error bars of differing lengths depending on particular probability - one can of course be completely confident that p , therefore error bars should not extend below zero or above 1.

0


1



62

3.6 Validation using Analysis 3.6.1 Queueing Theory and its Applicability to Call Oriented Traffic Queueing theory has been extensively studied by e.g. Kleinrock and is detailed in [54]. To a non-initiated reader, the use of queues and queueing theory to model the behaviour of a call oriented scenario may be difficult to appreciate, since queueing in the conventional sense is not desirable due to the small delay that can be tolerated. The aim of this section is to provide an introduction to queueing theory and to show through classic telephony examples its applicability to UFDMA.

Terminology The term ‘queue’ is used to describe the behaviour of a system where items arrive and depart according to particular probability distributions. In the simplest case this could be a First In First Out (FIFO) system, for example a supermarket queue or data buffer. In more general cases much more complex problems can be analysed e.g. the in the supermarket scenario: the cases where somebody in the back of the queue pushes to the front, or a customer leaves the queue without being served, or even the case where you have no ‘queue’ at all, so that customers are served at random. Most queues can be described with the aid of a five part descriptor [54]

A=B=m=K=M The A and B terms describe the interarrival and duration distributions respectively. A and B are replaced by the following symbols to describe particular distributions: M (exponential), Er (r stage Erlangian), HR (r stage hyperexponential), D (deterministic, e.g. constant) and G (general). The term m is used to specify the number of servers in the system, and in certain circumstances it is necessary to specify the amount of storage available at a server denoted by K . The final M is used to specify the size of the customer population. In many cases the last two parts of the descriptor are not specified specifically and in these cases are assumed to be infinity. Erlang B Formula - M=M=m=m Queue The Erlang B formula, sometimes referred to as ‘blocked calls cleared’ [52], has been one of the ways that the probability of blocking can be measured at a telephone exchange, when the number of users is significantly greater (infinite) than the number




63

of outgoing trunk lines m. Blocking in this situation is considered as being no outgoing trunk lines available at the instant a new call is initiated. The new call is then lost and will return a random time later with the same random interarrival time as any other user in the system. Two other formulae are used for the following specific situations:

The Engset distribution is used when the number of users is slightly larger than the number of outgoing trunks. This is covered in more detail in Chapter 8 when there are a limited number of nodes which have a high activity. This can be represented by a M=M=m=K=M descriptor; The Erlang C Formula is used when blocking is defined as no out-going trunks available within a specified time period. A infinite number of users is assumed and the formula can be represented by a M=M=m queue descriptor.

The behaviour of an M=M=m=m queue (leading to the Erlang B formula) can be described using a Markov chain as shown in Figure 3.9. Here the ellipse represents the number of servers or out going trunks, and the arrows between the each ellipse represent a single arrival or departure, such that the number of active servers increases or decreases by one respectively. λ 0

λ 1

µ

λ 2

.....

2µ

m-2

λ m-1

(m-1) µ

m mµ

Figure 3.9: A Markov Representation of the Erlang B formula (M=M=m=m queue). The Erlang B derivation assumes that the mean system arrival rate remains constant throughout. This can be achieved with an infinite population, with each member of the population having an infinitesimally small arrival rate. It is assumed that the interarrival times are negative exponentially distributed, which with an infinite population, means that the probability of k arrivals in a time t forms a Poisson process, given by [54]

Pk (t) =

t)k k!

(

exp (

t) k 0 t 0

(3.15)

It is also assumed that departures are negative exponentially distributed and that the rate of departures k increases linearly with the number of servers k out of m in use. This is intuitively obvious because if more customers are using a service then it is more likely that one will complete service in any time instant. Assuming that the arrivals and departures when the system is in state k are given by




k =

k