Performance Evaluation of Dynamic Spectrum ...

Performance Evaluation of Dynamic Spectrum Assignment and Access Technologies Stanislav Filin1, Hiroshi Harada1, Mikio Hasegawa2 1

National Institute of Information and Communications Technology, Yokosuka, Japan 2 Tokyo University of Science, Tokyo, Japan [email protected], [email protected], [email protected]

Abstract—Implementing dynamic spectrum assignment and access technologies in heterogeneous wireless networks may lead to considerable improvement in spectrum usage and quality of service. In this paper we evaluate the performance of SINR-based dynamic spectrum assignment and access algorithms using simulation. We compare network capacity values gained using these two algorithms with the one gained using SINR-based vertical handover algorithm. We show that dynamic spectrum assignment and access algorithms have considerable gain in network capacity compared to handover algorithm. These performance evaluation results prove the efficiency of the dynamic spectrum assignment and access technologies.

to define “building blocks comprising i) network resource managers, ii) device resource managers and iii) the information to be exchanged between the building blocks, for enabling coordinated network-device distributed decision making which will aid in the optimization of radio resource usage, including spectrum access control, in heterogeneous wireless access networks” [11].

Keywords-dynamic spectrum assignment, dynamic spectrum access, Cognitive Wireless Clouds (CWC)

In Dynamic Spectrum Assignment use case we assume that spectrum resource assigned to heterogeneous wireless network is limited. However, this spectrum resource can be dynamically distributed between wireless access nodes of the heterogeneous wireless network.

I.

INTRODUCTION

Fixed spectrum allocation appeared to be inefficient [1]. FCC measurements have indicated that 90% of the time, many licensed frequency bands remain unused [2]. This allows radio access networks and terminals to detect and use temporally unused spectrum, thus improving radio resource usage and quality of service (QoS). One of the technologies to exploit this opportunity is spectrum pooling or sharing. Spectrum pooling was first defined by J. Mitola III as “arrangement under which current owners of spectrum agree to rent it to each other for time periods as brief as one second” [3]. Such arrangements facilitate “white space” or “primary/secondary systems” concept. Under this concept, the secondary system uses unused spectrum of the primary system without disturbing its operation. System development under this concept has been performed within DARPA XG [4] and ORACLE [5] projects.

Different use cases are described in the IEEE P1900.4 working group [12]. In this paper, we concentrate on two use cases, that is, Dynamic Spectrum Assignment use case and Dynamic Spectrum Access use case.

In Dynamic Spectrum Access use case we assume that assignment of frequency bands to the wireless access nodes is fixed. We also assume that the heterogeneous wireless network serves cognitive terminals having multi-homing capability. Such terminals can support several simultaneous connections to heterogeneous wireless network. These terminals dynamically update their multiple connections with heterogeneous wireless network and dynamically distribute traffic between these connections while dynamically accessing frequency bands assigned to heterogeneous wireless network. Our goal in this paper is to evaluate the performance of heterogeneous wireless network using dynamic spectrum assignment and access technologies compared to the performance of heterogeneous wireless network using vertical handover technology.

Other technologies to improve spectrum usage are dynamic spectrum assignment and access. Dynamic spectrum assignment refers to the scenario when spectrum assignment within heterogeneous wireless network is dynamically rearranged. Dynamic spectrum access refers to the scenario when radio access networks (RANs) and cognitive terminals dynamically access different frequency bands assigned to heterogeneous wireless network.

For this purpose in this paper we use a class of signal-tointerference-plus-noise-ratio-(SINR)-based algorithms, including SINR-based handover algorithm, SINR-based dynamic spectrum assignment algorithm, and SINR-based dynamic spectrum access algorithm. SINR-based dynamic spectrum access algorithm used in this paper is described in [13]. SINR-based dynamic spectrum assignment algorithm is a novel one proposed in this paper.

Dynamic spectrum assignment and access technologies have been studied in the E2R [6], [7] and Cognitive Wireless Clouds (CWC) [8] – [10] projects. Following these activities, IEEE P1900.4 working group was launched in February 2007

To evaluate the performance of heterogeneous wireless network employing vertical handover, dynamic spectrum assignment, and dynamic spectrum access technologies we use simulation in this paper. We show that considered dynamic

978-1-4244-2644-7/08/$25.00 © 2008 IEEE

spectrum assignment and access algorithms have considerable gain in network capacity compared to handover algorithm. The rest of the paper is organized as follows. Section 2 presents dynamic spectrum assignment and access scenarios considered in this paper. Section 3 briefly describes SINRbased handover, dynamic spectrum assignment, and dynamic spectrum access algorithms analyzed in this paper. Section 4 presents simulation results. Section 5 concludes this paper. II.

DYNAMIC SPECTRUM ASSIGNMENT AND ACCESS SCENARIOS

Dynamic spectrum assignment and access scenarios considered in this paper are shown in Fig. 1 and 2. Operator A

Operator B

Operator A

Operator B

RAN 1

RAN 2 RAN 3

RAN 1

RAN 2 RAN 3

F1

F2

F3

F1

CT

F2

F3

CT

Figure 1. Dynamic spectrum assignment scenario. Operator A

Operator B

Operator A

Operator B

RAN 1

RAN 2 RAN 3

RAN 1

RAN 2 RAN 3

F1

F2

F3

CT

F1

F2

F3

CT

Heterogeneous wireless network consists of I wireless access nodes. These nodes may belong to different radio access networks and use different radio interfaces. Heterogeneous wireless network has limited downlink and uplink resource for downlink and uplink transmission S DL,max and SUL ,max . We denotes parts of these resources assigned to wireless access node i for downlink and uplink transmission as S DL,max ( i ) and SUL,max ( i ) . They correspond to some reference time intervals, for example, frame durations. In case of dynamic spectrum assignment algorithm, values of S DL,max ( i ) and SUL,max ( i ) can be dynamically changed, while for dynamic spectrum access algorithm they are fixed. Heterogeneous wireless network serves J cognitive terminals. Each cognitive terminal has K active radio interface modules. Radio interface used by particular radio interface module is determined by wireless access node this radio interface module is connected to. Each cognitive terminal has maximum transmission power. This transmission power is shared by all the active radio interface modules of the cognitive terminal. Each cognitive terminal has downlink and uplink QoS requirements. In this paper, we consider one QoS parameter only, that is, minimum average data rate. Downlink and uplink minimum average data rate values are defined as RDL,min ( j )

and RUL,min ( j ) .

Adaptive coding and modulation is used in the wireless system considered. For the given radio interface and adaptive coding and modulation algorithm, downlink and uplink data rates RDL and RUL experienced by cognitive terminals are functions of corresponding SINR values

z DL ( i, j )

and

Figure 2. Dynamic spectrum access scenario.

zUL ( i, j ) .

In dynamic spectrum assignment scenario, spectrum resources assigned to RANs within heterogeneous wireless network are dynamically changed. In the example of Fig. 1, a part of spectrum resource F1 is re-assigned to spectrum resource F2 to balance load of these two spectrum resources.

B. Handover Algorithm In this paper we consider SINR-based vertical handover algorithm described, for example, in [14].

In dynamic spectrum access scenario, cognitive terminal having multi-homing capability dynamically access spectrum resources assigned to different RANs. During this dynamic spectrum access, cognitive terminal dynamically reconfigure its active connections to RANs. In the example of Fig. 2, this reconfiguration helps to balance load of spectrum resources F1 and F2. III.

HANDOVER, DYNAMIC SPECTRUM ASSIGNMENT, AND DYNAMIC SPECTRUM ACCESS ALGORITHMS

A. System Description In this paper, heterogeneous wireless system comprised of heterogeneous wireless network and cognitive terminals is considered.

According to this algorithm, cognitive terminal j select its serving wireless access node as

(

)

i * ( j ) = max RDL ( zDL ( i , j ) ) . i =1,…, I

(1)

In other words, each cognitive terminal selects the wireless access node i * ( j ) with the maximum downlink data rate RDL for the given SINR z DL ( i * ( j ) , j ) . This allows taking into

account different coding and modulation schemes of different radio interfaces used in heterogeneous wireless network.

C. Dynamic Spectrum Assignment Algorithm In this paper we consider the following SINR-based dynamic spectrum assignment algorithm.

First, each cognitive terminal selects one serving wireless access node according to SINR-based vertical handover algorithm (1). Then, for each wireless access node we calculate the number of cognitive terminals L ( i ) that are served by this wireless access node i . Finally, we downlink and uplink spectrum resources are assigned to wireless access nodes of heterogeneous wireless network as I

S DL,max ( i ) = S DL,max ⋅ L ( i )

∑ L (i ) ,

SUL,max ( i ) = SUL,max ⋅ L ( i )

∑ L (i ) .

(2)

i =1 I

(3)

i =1

D. Dynamic Spectrum Access Algorithm In this paper we consider SINR-based dynamic spectrum access algorithm presented in [13]. Here, we give brief description of this algorithm. First, for each cognitive terminal, list of candidate wireless access nodes is created as

{i : R ( z (i , j ) ) > 0} . *

*

DL

(4)

DL

Then, for each cognitive terminal, we sort its candidate wireless access nodes as

( ) ( ) ( ) ( z (i , j ) ) > R ( z ( i , j )) > … R ( z (i , j ) ) .

RDL z DL ( i1* , j ) > RDL zDL ( i2* , j ) > … RDL zDL ( iM* , j ) , RUL

UL

* 1

UL

* m

* n

UL

* 2

UL

UL

* N

vertical handover when required. Spectrum resource of each wireless access node is fixed. In dynamic spectrum access scenario, cognitive terminals have multi-homing capability, that is, they can support several simultaneous connections to heterogeneous wireless network. In the simulation, the maximum number of connections per cognitive terminal is equal to two connections. These cognitive terminals pseudo-randomly move within the coverage area of the heterogeneous wireless network. During movement, they dynamically update their multiple connections with heterogeneous wireless network and dynamically distribute traffic between these connections according to SINR-based dynamic spectrum access algorithm. Spectrum resource of each wireless access node is fixed. In dynamic spectrum assignment scenario, cognitive terminals pseudo-randomly move within the coverage area of the heterogeneous wireless network performing SINR-based vertical handover when required. However, total spectrum resource of heterogeneous wireless network is dynamically distributed between wireless access nodes. Network topology includes seven wireless access nodes (see Fig. 3). Distance between each two wireless access nodes is 2000 m. Mobility is simulated as follows. Within the 3000 m radius around central wireless access node, 20 reference points are pseudo-randomly allocated, different for different cognitive terminals. Each cognitive terminal moves between its reference points with constant velocity 10 m/s.

(5)

3000

(6)

Wireless Access Node

Here i and i belongs to the set of candidate wireless access nodes (4); N ≤ K and M ≤ K , that is, the number of active connections is limited by the number of available radio interface modules within a cognitive terminal. To minimize the used resource, it is better to transmit all data via the connection with the maximum data rate. This corresponds to the first wireless access node in the sorted list. However, resource of each wireless access node is limited.

2000

1000

0 -3000

-2000

-1000

We sequentially distribute data to be transmitted between the wireless access nodes (5), (6) using the iterative procedure described in [13].

0

1000

2000

3000

-1000

-2000

IV.

SIMULATION RESULTS

We evaluate the performance of heterogeneous wireless system employing handover, dynamic spectrum assignment, and dynamic spectrum access algorithms using simulation. Detailed description of the simulation system used is presented in [13]. Here we give brief description of this simulation system and simulation scenarios.

Trajectory of one Cognitive Terminal -3000 Figure 3. Network topology.

Three simulation scenarios are considered, that is, legacy wireless system scenario, dynamic spectrum access scenario, and dynamic spectrum assignment scenario.

Frequency division duplex and TDMA multiple access are considered with single carrier transmission scheme. In this case, downlink and uplink are independent. We consider uplink during simulation.

In legacy wireless system scenario, cognitive terminals pseudo-randomly move within the coverage area of the heterogeneous wireless network performing SINR-based

Total spectrum resource assigned for uplink transmission is equal to 70 MHz. In the first two scenarios, each wireless access node has 10 MHz uplink bandwidth. In the dynamic

Adaptive coding and modulation is considered in this simulation. We use tail-biting convolutional coding with constraint length K = 7 and coding rates 1/ 2, 2 / 3, 3 / 4 and variable M-QAM ( M = 4,16, 64 ) modulation as basis for constructing six following coding and modulation schemes: QPSK 1/2, QPSK 3/4, 16-QAM 1/2, 64-QAM 1/2, 64-QAM 2/3, 64-QAM 3/4.

Handover 250

DS Access DS Assignment

200 150 100 50 0 0

Assuming maximum throughput adaptive transmission, spectrum efficiency as function of signal-to-noise ratio (SNR) for these coding and modulation schemes is shown in Fig. 4. This curve obtained by link level simulation in AWGN channel is used as function RUL ( zUL ( i, j ) ) in the simulation. Also, this curve is used for cell planning of the wireless network considered. For cell planning, we assume that at 2000 m from wireless access node, SNR is equal to 0 dB assuming maximum transmission power. 5 Spectral efficiency, b/s/Hz

300

4

50

100 150 200 Network traffic load, Mb/s

250

300

Figure 5. Uplink network throughput as function of uplink network traffic load. 1.1 User trhoughput, Mb/sddd

• • • • • •

uplink network capacity value in Fig. 5. QoS-guaranteed network capacity is equal to maximum value of network throughput when QoS requirements (data rate in our case) are still satisfied for all users in the network.

Network trhoughput, Mb/sddd

spectrum assignment scenario, total spectrum of 70 MHz is dynamically distributed between these 7 wireless access nodes. Frequency reuse factor is seven, which means that we do not simulate inter-cell interference.

1 0.9 0.8 0.7

Handover DS Access

0.6

DS Assignment 0.5

3

0

2

50

100 150 200 Network traffic load, Mb/s

250

300

Figure 6. Uplink throughput per user as function of uplink network traffic load.

1 0 0

5

10 SNR, dB

15

20

Figure 4. Spectral efficiency as a function of SNR.

For each of three simulation scenarios considered, we additionally considered different uplink traffic rate per cognitive terminal. The traffic rates simulated are 0.5 Mb/s, 1 Mb/s, 2 Mb/s, and 5 Mb/s per cognitive terminal. Considered application is constant bit rate one. Fig. 5 and 6 show examples of uplink network throughput and uplink throughput per user as functions of uplink network traffic load for three scenarios considered for uplink traffic rate per terminal equal to 1 Mb/s. Uplink network throughput is equal to the sum of uplink throughput of all 7 wireless access nodes. Uplink throughput per user is equal to uplink network throughput divided by number of terminals in network. Uplink network traffic load is equal to uplink traffic rate per terminal multiplied by number of terminals in the network. From Fig. 6 it can be seen that with increase in uplink network traffic load, uplink throughput per user start to degrade from some point. This point corresponds to QoS-guaranteed

Table 1 summarizes the values of QoS-guaranteed network capacity for three simulation scenarios considered. Fig. 7 shows the gain in QoS-guaranteed network capacity of the dynamic spectrum assignment and access algorithms compared to handover algorithm as a function of traffic rate per cognitive terminal. TABLE I. Traffic rate per cognitive terminal, Mb/s 0.5 1 2 5

QOS-GUARANTEED NETWORK CAPACITY QoS-guaranteed network capacity, Mb/s Handover

Dynamic spectrum assignment

Dynamic spectrum access

80 70 55 45

125 110 90 80

210 200 155 110

The following observations can be done from Table 1 and Fig. 7. SINR-based dynamic spectrum access algorithm has considerable gain in QoS-guaranteed network capacity compared to SINR-based handover algorithm. This gain is equal to 56% – 78% depending on traffic rate per terminal. Gain in network capacity increases with the increase in traffic

rate per terminal as dynamic spectrum access algorithm can distribute traffic load between several wireless access nodes.

These performance evaluation results prove the efficiency of the dynamic spectrum assignment and dynamic spectrum access technologies compared to vertical handover technology.

Gain in network capacity, %sss

200

REFERENCES [1]

150

[2]

Gain for DS Access Gain for DS Assignment 100

[3]

50 0

0.5

1

1.5 2 2.5 3 3.5 4 Traffic rate per cognitive terminal, Mb/s

4.5

5

5.5

[4] [5]

Figure 7. Gain in QoS-guaranteed network capacity of dynamic spectrum assignment and dynamic spectrum access algorithms compared to handover algorithm as a function of traffic rate per cognitive terminal.

[6]

SINR-based dynamic spectrum assignment algorithm has further gain in QoS-guaranteed network capacity compared to SINR-based handover algorithm. This gain is equal to 144% – 188%.

[7]

V.

CONCLUSIONS

Dynamic spectrum assignment and access technologies provide great opportunities to both users and operators to improve users’ experience and increase network capacity. In this paper we have evaluated the performance of SINRbased dynamic spectrum assignment and access algorithms using simulation. We have compared network capacity values gained using these two algorithms with the one gained using SINR-based vertical handover algorithm. We show that dynamic spectrum assignment and access algorithms have considerable gain in network capacity compared to handover algorithm. SINR-based dynamic spectrum access algorithm has gain in QoS-guaranteed network capacity compared to SINR-based handover algorithm equal to 56% – 78%. SINR-based dynamic spectrum assignment algorithm has gain in QoS-guaranteed network capacity compared to SINR-based handover algorithm equal to 144% – 188%.

[8]

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[14]

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