Neural Fuzzy Call Admission and Rate Controller for ... - CiteSeerX

Neural Fuzzy Call Admission and Rate Controller for WCDMA Cellular Systems Providing Multirate Services Chung-Ju Chang

Li-Chung Kuo

Yih-Shen Chen

Scott Shen

Department of Communication Engineering, National Chiao Tung University, Taiwan 1001 Ta Hsueh Road, Hsinchu 300, Taiwan Tel. No.: 886-3-5731923, Fax No.: 886-3-5710116 [email protected]

[email protected] [email protected] [email protected]

model to maintain system stability with minimum outage probability. According to the residual capacity of the home and adjacent cells, the system can measure the current level of multiple access interference (MAI) and then decide a new call is accepted or blocked. These schemes used the interference at the present time instant to determine the acceptance of the call request. However, the present interference will change right after the acceptance of a new call. It makes the decision imprecise.

ABSTRACT The paper proposes a neural fuzzy call admission and rate controller (NFARC) scheme for WCDMA cellular systems providing multirate services. The NFARC scheme can guarantee the quality of service (QoS) requirements and improve the utilization of the system. Simulation results show that the NFARC scheme achieves low forced termination probability and high system capacity even in the bursty traffic conditions. NFARC accepts users more than intelligent call admission controller (ICAC) by an amount of 45.35%.

Shen et al. [3] proposed an intelligent call admission controller (ICAC) for WCDMA cellular systems to support differentiated quality of service provisioning. In the scheme, pipeline recurrent neural network (PRNN) accurately predicts the next-step existingcall interference, and fuzzy call admission processor makes admission decision for the call request. However, ICAC does not consider the conditions of adjacent cells. Accepting a user near the cell boundary can make the interference experienced by the adjacent cell higher and increase the outage probability more, even if the home cell interference is low. Besides, one of the promising important applications of WCDMA systems is the World Wide Web (WWW) transfers. The traditional Poisson traffic model is not suitable for the Internet service. The WWW traffic is bursty on many or all time scales. It can be modeled as a self-similar process. ICAC cannot serve such bursty traffic. Most data users with bursty traffic will be rejected because ICAC allows the bursty traffic to be transmitted without delay. Since data services can tolerate delays, a rate control scheme should be adopted to overcome this situation.

Categories and Subject Descriptors C.2.1 [Computer-Communication Networks]: Architecture and Design –wireless communication.

Network

General Terms Performance, Design.

Keywords Call admission control (CAC), WCDMA, Neural fuzzy.

1. INTRODUCTION Number-based CAC and interference-based CAC are two basic methods for call admission control (CAC). With respect to the interference-limited attribute of WCDMA cellular systems, the interference-based CAC method is more suitable. In WCDMA systems, the system load of uplink can be regarded as the total interference that the base station (BS) receives, and an acceptance of a new call will cause an increment of the interference.

In [4], Lo, Chang, and Shung used a neural fuzzy call-admission and rate controller (NFCRC) to decide whether a call request is accepted or not. NFCRC can guarantee the QoS and provide an appropriate rate for users. Instead of the rate control in [5] that the transmission rate is adaptively modified from one time slot to another, the rate control in [4] allocates the transmission rate for the user until the connection is finished, which is inappropriate.

Shin, Cho, and Sung [1] proposed an interference-based channel assignment scheme for DS-CDMA cellular systems. Instead of a fixed link capacity, the scheme calculates the current interference margin and the handoff interference margin. If the interference after the channel assignment is below the allowed level which is determined by the network, a new channel is assigned to the new call. In [2], Dimitriou and Tafazolli developed a mathematical

The paper proposes a neural fuzzy call admission and rate controller (NFARC) for WCDMA cellular systems supporting multirate services. It contains a PRNN/ERLS interference predictor to predict the next time instant existing-call interference, and a neural fuzzy call admission and rate controller to determine whether the call request is accepted or not and the transmission rate assigned at the beginning of each data burst. An extended recursive least squares (ERLS) training algorithm for the PRNN has been shown to achieve significantly higher prediction precision values. The neural network can make the fuzzy logic systems more adaptive and effective. Adaptive network based

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383

fuzzy inference system (ANFIS) is a good choice to fine-tune the membership functions of the fuzzy logic systems, which will be used in this paper.

Ia ( n) =

$

data burst. According to I k ( n + 1) and I a ( n ) , a proper transmission rate will be allocated to the data burst. If packet cannot be transmitted in one frame time, the remaining bits of the packet will be transmitted at the next frame time obeying the assigned transmission rate until all of the bits are transmitted. To storage the packets waiting for transmitting, the buffer for data service are provided. * denoted by Potgi , is set to be the system QoS requirements. In

order to protect the handoff connection against forced termination, the required forced termination probability, denoted by Pf* , is also set to be the QoS requirement.

Accept / Reject

PRNN/ERLS Interference Predictor

(2)

In the paper, the required outage probability for type- i traffic,

Potg1 ( n )

I k ( n)

}

> Lth ,

Voice service requires low delay and less bandwidth; on the other hand, data service can tolerate moderate delay and adopt a variable rate transmission. For data source, the rate control scheme will assign the transmission rate at the beginning of each

Pf ( n )

$ I k ( n + 1)

( n ) Lab

to the base station of adjacent cell b , and Lth is the threshold that the call request can cause significant influence to the adjacent cell base station.

2. NFARC 2.1 System Block Diagram

Ia ( n)

ab

present time instant n , Lab is the link gain from the call request

The rest of the paper is organized as follows. Section II gives the designs for the neural fuzzy call admission and rate controller. The simulation results and discussions are presented in section III. Finally, concluding remarks are given in section IV.

Neural Fuzzy Call Admission and Rate Controller

{I

where I ab ( n ) is the interference mean of adjacent cell b at the

Considering the adjacent cell interference, the NFARC will monitor the adjacent cells’ residual capacity by the radio network controller (RNC) and make proper admission decision, which can avoid the outage condition of adjacent cell. The next time instant existing-call interference is used to avoid the outage condition of home cell. The data source models, which include batch Poisson process and Pareto distributed process, are taken into account in the systems, individually. And, in order to increase the system capacity and maintain the QoS requirements, the rate controller is used to determine the transmission rate assigned at the beginning of each data burst. Simulation results show that NFARC guarantees the QoS requirements and accepts users more than ICAC by an amount of 45.35%.

Potg 2 ( n )

Max

∀b∈{adjacent cell}

2.2 Neural Fuzzy CAC and Rate Controller

Rate control for Non-real-time Data

A five-layer neural fuzzy controller, which is implemented by adaptive-network-based fuzzy inference system (ANFIS) [7], is adopted to design the NFARC. The ANFIS employs the adaptive network architecture to represent the fuzzy inference system, which can be applied to a wide range of areas, such as nonlinear function modeling, time series prediction, and fuzzy controller design. Z

Figure 1. System block diagram Figure 1 depicts the block diagram of the proposed neural fuzzy call admission and rate controller (NFARC) and a PRNN/ERLS interference predictor. The PRNN/ERLS interference predictor [6] takes the interference mean of cell k at the present time

Q5

Σ

Layer 5

instant n , I k ( n ) , as an input variable to accurately predict the

Q4,1

$

interference mean at the next time instant ( n + 1) , I k ( n + 1) . The

Q4,32

Layer 4

I k ( n ) is obtained by

．．．

Q3,1 Q3,2 Layer 3

N −1

I k ( n ) = ∑ j = 0 I k ( n − jT ) N ,

Ν

Q3,32

Ν

Ν

Ν

．．．

Ν

Ν

Π

Π

Π

．．．

Π

Π

(1) Q2,1

where N is the size of time window, and I k ( q ) is the received

Layer 2

interference power at time instant q . The NFARC chooses the forced termination probability for handoffs measured at present time n , denoted by Pf ( n ) , the outage probabilities of type-1 and

Q2,32

Π

Q1,1 Layer 1

type-2 services measured at the present time n , denoted by Potg1 ( n ) and Potg 2 ( n ) , the influence of a call request on the $

adjacent cell base stations, denoted by I a ( n ) , and I k ( n + 1) as

Sa f

Q1,2 Ns f

Pf ( n )

input linguistic variables to determine the acceptance for the call request. If the call request is a non-real-time data, the NFARC further assigns an appropriate rate for the user. Notice that I a is given by

Q1,10 Sa1

Ns1

Potg1 ( n )

Sa2

Ns2

Potg 2 ( n )

Sm

Lg

Wk

$ I k ( n + 1)

Sr

Ia ( n)

Figure 2. The structure of the NFARC The fuzzy inference system under consideration has five linguistic $

variables ( Pf ( n ) , Potg1 ( n ) , Potg 2 ( n ) , I k ( n + 1) , and I a ( n ) ) and

384

each variable is divided into two fuzzy terms. Figure 2 shows the corresponding equivalent ANFIS architecture, which represents the first-order Sugeno fuzzy model. The node functions of each layer are similar and they are described as below:

influence of the call request on the adjacent cell base stations, and the predicted system load. For the handoff call request, the NFARC considers the same input variables as for the new call request except I a ( n ) since the influence of a handoff call on the

Layer 1: Every node i in this layer is an adaptive node with a node function

adjacent cell base stations has already in the system. The adjacent cell has already considered that I a ( n ) is included in its

(

)

⎧μ P ( n) , ⎪ Sa f f ⎪ M Q1, i = ⎨ ⎪ ⎪ μ Sr I a ( n ) , ⎩

(

)

interference. For the rate control, the NFARC considers I a ( n )

for i = 1

,

$

and I k ( n + 1) as its input linguistic variables. These two

(3)

parameters indicate the system loading. The assigned transmission rate for data user will be reduced if the system loading is high.

for i = 10

$

where Pf ( n ) ( Potg1 ( n ) , Potg 2 ( n ) , I k ( n + 1) , or I a ( n ) ) is the input

The details of Layer 1 are described as follows. Term sets and membership functions of input linguistic variables are defined in Table 1, in which trapezoidal function h ( x; x0 , x1, a0 , a1 ) is chosen

to the node i , and Sa f ( Ns f , Sa1 , Ns1 , Sa2 , Ns2 , Sm , Lg , Wk , or Sr ) is a linguistic term associated with this node. μ Sa f ,L , μ Sr

to be the membership function and is given by

are the membership functions for the terms Sa f ,L , Sr . Each node

⎧( x − x0 ) a0 + 1 for x0 − a0 < x ≤ x0 ⎪ 1 for x0 < x ≤ x1 ⎪ , h ( x; x0 , x1 , a0 , a1 ) = ⎨ ⎪( x1 − x ) a1 + 1 for x1 < x ≤ x1 + a1 ⎪ otherwise 0 ⎩

function specifies the degree to which the given input Pf ( n ) $

( Potg1 ( n ) , Potg 2 ( n ) , I k ( n + 1) , or I a ( n ) ) satisfies the qualifier Sa f ( Ns f , Sa1 , Ns1 , Sa2 , Ns2 , Sm , Lg , Wk , or Sr ).

where x0 ( x1 ) in h ( ⋅) is the left (right) edge of the trapezoidal

Layer 2: Every node i in this layer is a fixed node labeled Π . The output of node i , denoted by Q2,i , is the product of all the

function, and a0 ( a1 ) is the left (right) width of the trapezoidal function.

incoming signals for the i -th rule. It is given by

Q2,i

Table 1. The term sets and their membership functions

⎧ ⎛$ ⎞ ⎪ μ Sa f Pf ( n ) × μ Sa1 Potg1 ( n ) × μ Sa2 Potg 2 ( n ) × μ Sm ⎜ I k ( n + 1) ⎟ × μWk I a ( n ) , ⎝ ⎠ ⎪ ⎪⎪ =⎨ M ⎪ ⎪ ⎛$ ⎞ ⎪ μ Ns f Pf ( n ) × μ Ns1 Potg1 ( n ) × μ Ns2 Potg 2 ( n ) × μ Lg ⎜ I k ( n + 1) ⎟ × μ Sr I a ( n ) , ⎝ ⎠ ⎩⎪

(

)

(

)

(

)

(

)

for i = 1

(

)

(

)

(

)

(

)

for i = 32

Elements of term sets

.(4) Pf ( n )

Each node output represents the firing strength of i -th rule and performs the fuzzy AND operation.

Potg1 ( n )

Layer 3: Every node i in this layer is a fixed node labeled Ν . The i -th node calculates the ratio of the i -th rule’s firing strength to the sum of all rules’ firing strengths. The output of node i , denoted by Q3,i , is called normalized firing strength and

∑ j =1 w j . 32

Potg 2 ( n )

(6)

Ia ( n)

and pi , qi , ri are the consequent parameter sets of node i .

Q5 = ∑

=∑

=∑

∑

32 w j =1 j

.

f

f

f

1

1

2

) (

μ Sm ⎛⎜ I k ( n + 1) ⎞⎟ = h ⎛⎜ I k ( n + 1) ; 0, Sme , 0, Smw ⎞⎟

Large( Lg )

$ $ μ Lg ⎛⎜ I k ( n + 1) ⎞⎟ = h ⎛⎜ I k ( n + 1) ; I o , ∞, Lg w , 0 ⎞⎟ ⎝ ⎠ ⎝ ⎠

Weak( Wk )

μWk ( I a ) = h ( I a ;0, Wke , 0, Wkw )

Strong( Sr )

μ Sr ( I a ) = h ( I a ; I o , ∞, Srw , 0 )

⎝

$

⎠

⎝

$

⎠

less than Nse f by a safety amount for separating the satisfactory * region and the violation region; Nse1 ( Nse 2 ) is set to be Potg 1

(7)

* ( Potg 2 ) minus a safety margin, and Sae1 ( Sae 2 ) is also set to a

For the new call request, the NFARC considers the Pf ( n ) ,

value less than Nse1 ( Nse 2 ) for the same reason as the safety

$ Potg1 ( n ) , Potg 2 ( n ) , I a ( n ) , and I k ( n + 1) as its input linguistic

amount for Sae f ; I o is the tolerable interference power

variables which indicate the system performance measures, the

corresponding to the minimal signal-to-interference power ratio

385

)

Small( Sm )

As for the parameter setting for these membership functions, Nse f is set to be Pf* minus a safety margin, and Sae f is a value

Layer 5: The single node in this layer is a fixed node labeled Σ , which computes the overall output Q5 as the summation of all incoming signals. 32 wf i =1 i i

Satisfied ( Sa2 )

$ I k ( n + 1)

where Q4,i is the output, fi is a crisp output in the consequent,

32 w f i =1 i i

Satisfied ( Sa1 )

( ) ( ) μ Ns ( Pf ( n ) ) = h ( Pf ( n ) ; Nse ,1, Nsw , 0 ) μ Sa ( Potg1 ( n ) ) = h ( Potg1 ( n ) ;0, Sae1, 0, Saw1 ) μ Ns ( Potg1 ( n ) ) = h ( Potg1 ( n ) ; Nse1,1, Nsw1, 0 ) μ Sa ( Potg 2 ( n ) ) = h ( Potg 2 ( n ) ;0, Sae 2 , 0, Saw2 ) μ Sa f Pf ( n ) = h Pf ( n ) ;0, Sae f , 0, Saw f

(

Layer 4: Every node i in this layer is an adaptive node with a node function

32 Q i =1 4, i

Not satisfied ( Ns f )

Membership function

Not satisfied ( Ns2 ) μ Ns2 Potg 2 ( n ) = h Potg 2 ( n ) ; Nse 2 ,1, Nsw2 , 0

(5)

Q4,i = wi × fi = wi × ( pi × X + qi × Y + ri ) , 1 ≤ i ≤ 32 ,

Satisfied ( Sa f )

Not satisfied ( Ns1 )

calculated as Q3,i = wi = wi

(8)

the system can accept the new call request. At any specific system load condition, the system will tend to reject the bad users with larger influence on the adjacent cell base stations according to I a ( n ) . Tables 4 and 5 list the fuzzy inference rules of handoff

SIR1* . Sme and Wke would be sets to be fraction of I o . We also

set Smw = I o − Sme = Lg w , Wkw = I o − Wke = Srw to simplify the design of fuzzy logic parameters. The other endpoints of Saw f , Nsw f , Saw1 , Nsw1 , Saw2 , and Nsw2 must be fine-tuned to proper

voice call and handoff data call. Since dropping an ongoing call is regarded to be more annoying than blocking a new call, it is needed to give a high priority to the handoff calls as compared to new calls. The system tends to accept the handoff call as the Pf ( n ) is not satisfied more likely than as Pf ( n ) is satisfied to

values during simulations. We use ANFIS to tune all of these membership functions. Table 2. The rule structure for new voice call request

protect the ongoing call anyway except the condition that the traffic load is very heavy. And the larger Potg1 ( n ) , Potg 2 ( n ) and

Rule

Pf

Potg 1

Potg 2

$ I n +1

Ia

Z nv

Rule

Pf

Potg 1

Potg 2

$ I n +1

Ia

Z nv

1

Sa f

Sa1

Sa 2

Sm

-

SAnv

14

Ns f

Sa1

Sa 2

Sm

-

SAnv

2

Sa f

Sa1

Sa 2

Lg Wk

SAnv

15

Ns f

Sa1

Sa 2

Lg Wk

SAnv

3

Sa f

Sa1

Sa 2

Lg

Sr

WAnv

16

Ns f

Sa1

Sa 2

Lg

Sr

WAnv

$ I k ( n + 1) and I a ( n ) imply the lighter traffic load, then the system

4

Sa f

Sa1

Ns 2

Sm Wk

SAnv

17

Ns f

Sa1

Ns 2

Sm Wk

WAnv

5

Sa f

Sa1

Ns 2

Sm

Sr

WAnv

18

Ns f

Sa1

Ns 2

Sm

Sr

WRnv

6

tends to assign a higher transmission rate to the data call. Otherwise, a lower transmission rate will be assigned. Table 6 lists these fuzzy inference rules.

Sa f

Sa1

Ns 2

Lg Wk

WAnv

19

Ns f

Sa1

Ns 2

Lg Wk

WRnv

7

Sa f

Sa1

Ns 2

Lg

Sr

WRnv

20

Ns f

Sa1

Ns 2

Lg

Sr

SRnv

8

Sa f

Ns1

Sa 2

Sm

-

WAnv

21

Ns f

Ns1

Sa 2

Sm

-

WRnv

Rule

Pf

Potg 1

Potg 2

$ I n +1

Z hv

Rule

Pf

Potg 1

Potg 2

$ I n +1

Z hv

9

Sa f

Ns1

Sa 2

Lg Wk

WRnv

22

Ns f

Ns1

Sa 2

Lg

-

SRnv

1

Sa f

Sa1

Sa 2

-

SAhv

7

Ns f

Sa1

Ns 2

Sm

SAhv

10

Sa f

Ns1

Sa 2

Lg

Sr

SRnv

23

Ns f

Ns1

Ns 2

Sm Wk

WRnv

2

Sa f

Sa1

Ns 2

-

WRhv

8

Ns f

Sa1

Ns 2

Lg

WAhv

11

Sa f

Ns1

Ns 2

Sm Wk

WAnv

24

Ns f

Ns1

Ns 2

Sm

Sr

SRnv

3

Sa f

Ns1

Sa 2

Sm

WRhv

9

Ns f

Ns1

Sa 2

Sm

SAhv

12

Sa f

Ns1

Ns 2

Sm

Sr

SRnv

25

Ns f

Ns1

Ns 2

Lg

-

SRnv

4

Sa f

Ns1

Sa 2

Lg

SRhv

10

Ns f

Ns1

Sa 2

Lg

WAhv

13

Sa f

Ns1

Ns 2

Lg

-

SRnv

5

Sa f

Ns1

Ns 2

-

SRhv

11

Ns f

Ns1

Ns 2

-

SRhv

6

Ns f

Sa1

Sa 2

-

SAhv

$ I k ( n + 1) imply the heavier traffic load, then the system tends to

reject the handoff call request. For rate controller, the smaller

Table 4. The rule structure for handoff voice call request

Table 3. The rule structure for new data call request Rule

Pf

Potg 1

Potg 2

$ I n +1

Ia

Z nd

Rule

Pf

Potg 1

Potg 2

$ I n +1

Ia

Z nd

1

Sa f

Sa1

Sa 2

Sm

-

SAnd

13

Sa f

Ns1

Ns 2

Lg

-

SRnd

Rule

Pf

Potg 1

Potg 2

$ I n +1

Z hd

Rule

Pf

Potg 1

Potg 2

$ I n +1

Z hd

2

Sa f

Sa1

Sa 2

Lg Wk

WAnd

14

Ns f

Sa1

Sa 2

Sm

-

SAnd

1

Sa f

Sa1

Sa 2

-

SAhd

7

Ns f

Sa1

Ns 2

Sm

SAhd

3

Sa f

Sa1

Sa 2

Lg

Sr

WRnd

15

Ns f

Sa1

Sa 2

Lg Wk

WAnd

2

Sa f

Sa1

Ns 2

-

WRhd

8

Ns f

Sa1

Ns 2

Lg

WRhd

4

Sa f

Sa1

Ns 2

Sm Wk

WAnd

16

Ns f

Sa1

Sa 2

Lg

Sr

WRnd

3

Sa f

Ns1

Sa 2

Sm

WRhd

9

Ns f

Ns1

Sa 2

Sm

SAhd

5

Sa f

Sa1

Ns 2

Sm

Sr

WRnd

17

Ns f

Sa1

Ns 2

Sm Wk

WAnd

4

Sa f

Ns1

Sa 2

Lg

SRhd

10

Ns f

Ns1

Sa 2

Lg

WRhd

6

Sa f

Sa1

Ns 2

Lg Wk

WRnd

18

Ns f

Sa1

Ns 2

Sm

Sr

SRnd

5

Sa f

Ns1

Ns 2

-

SRhd

11

Ns f

Ns1

Ns 2

-

SRhd

7

Sa f

Sa1

Ns 2

Lg

Sr

SRnd

19

Ns f

Sa1

Ns 2

Lg

-

SRnd

6

Ns f

Sa1

Sa 2

-

SAhd

8

Sa f

Ns1

Sa 2

Sm

-

WRnd

20

Ns f

Ns1

Sa 2

Sm Wk

WRnd

9

Sa f

Ns1

Sa 2

Lg Wk

WRnd

21

Ns f

Ns1

Sa 2

Sm

Sr

SRnd

10

Sa f

Ns1

Sa 2

Lg

Sr

SRnd

22

Ns f

Ns1

Sa 2

Lg

-

SRnd

11

Sa f

Ns1

Ns 2

Sm Wk

WRnd

23

Ns f

Ns1

Ns 2

-

-

SRnd

12

Sa f

Ns1

Ns 2

Sm

SRnd

Sr

Table 5. The rule structure for handoff data call request

Table 6. The rule structure for rate control at each burst Rule I$ n +1

1 2

Ia

Zr

Sm

Wk

HR

Sm

Sr

MR

Rule I$ n +1

3 4

Ia

Zr

Lg

Wk

BR

Lg

Sr

BR

In Layer 4, the term set for the output linguistic variable of new voice call request T ( Z = Z nv ) = {Straightly Accept, Weakly

The fuzzy rules in Layer 2 are described as follows. Tables 2 and 3 list the fuzzy inference rules of new voice call and new data call. The more (less) satisfied the Pf ( n ) , Potg1 ( n ) , Potg 2 ( n ) are and

Accept, Weakly Reject, Straightly Reject}= { SAnv , WAnv , WRnv , SRnv }. Membership functions for Z nv are denoted by M ( Z nv ) =

$ the smaller (larger) the I k ( n + 1) is, the higher (lower) likelihood

386

{ μ SAnv , μWAnv , μWRnv , μ SRnv }, where μ X ( Z nv ) = X , and X is

μ D p = 0.0083 second. The mean holding times for both voice and

SAnv , WAnv , WRnv , or SRnv .

data services are 90 seconds. The speed of mobile users is either V1 = 20 Km/Hr or V2 = 60 Km/Hr with equal probability. The moving direction is modeled by the angle τ with uniform distribution. The radio propagation parameters of θ and ζ are set to be 4 and 8dB [9], and the handoff margin is set to be 3dB.

A new voice call request can be accepted if Z nv is greater than an acceptance threshold znva , SRnv ≤ znva ≤ SAnv . Without a loss of generality, SRnv = 0 , SAnv = 1 , and let WRnv = ( SRnv + znva ) 2 , WAnv = ( SAnv + znva ) 2 . Similarly, the term set for the output

The effectiveness of the proposed NFARC is investigated by comparing it with the ICAC, which is proposed in [3]. Neural fuzzy call admission controller (NFCAC), which is NFARC without rate control, is compared, too. In the simulations, the QoS requirements including the outage probability of type-1, the outage probability of type-2, and the forced termination probability will be examined to see if they are satisfactory. Then, the mean number of users and packets per second transmitted in a cell will be compared.

linguistic variable of handoff voice call request T ( Z = Z hv ) = { SAhv , WAhv , WRhv , SRhv }, and membership functions for Z hv are denoted by M ( Z hv ) ={ μ SAhv , μWAhv , μWRhv , μ SRhv }, where μ X ( Z hv ) = X , and X is SAhv , WAhv , WRhv , or SRhv . A handoff

voice call request can be accepted if Z hv is greater than an acceptance threshold zhva , SRhv ≤ zhva ≤ SAhv . Similarly, we set SRhv = 0 , SAhv = 1 , and let WRhv = ( SRhv + zhva ) 2 , and WAnv =

( SAnv + znva )

3.2 Results and Discussions

2 . And the term set for the output linguistic variable

0.025

of data call request is similar as voice call request. The term set for the output linguistic variable of rate control T ( Z = Z r ) ={High

* Potg 1

Potg1

outage probability

0.02

Rate, Medium Rate, Basic Rate}={ HR , MR , BR }. Membership functions for Z r are similar as what we mentioned before. By using the MATLAB fuzzy logic toolbox, ANFIS can be implemented efficiently. Using the given input/output data set, the toolbox function anfisedit constructs a fuzzy inference system whose membership function parameters are tuned using a backpropagation algorithm. After the system is fine-tuned, the membership functions are derived and they will be used in the neural fuzzy call admission and rate controller.

0.015 0.01

* Potg 2

ICAC NFCAC NFARC

Potg 2

0.005 0 0

0.2

0.4

0.6

0.8

1

1.2

λv = d=0.5*λ λd = 0.5)λ ) new call arrival rate λλ (λ ( v=λ

Figure 3. Outage probabilities vs. new call arrival rate

3. SIMULATION RESULTS 3.1 Simulation Environment In the simulations, we consider a WCDMA cellular system with 49 hexagonal cells. Input traffic generated within mobile users is classified into two types of service as real-time voice (type-1) and non-real-time data (type-2). New voice and data calls arrive at the system according to Poisson distributions with average arrival rates of λv and λd , respectively. Every voice source is characterized by a two-state (ON and OFF) discrete-time Markov chain traffic model and will generate one air-interface packet in each frame time of T = 10 ms during ON state (talk spurts) but none during OFF state (silence). The mean durations of talk spurts and silence periods are assumed to be exponentially distributed with 1/ α = 1 second and 1/ β = 1.35 seconds, respectively.

forced termination probability

0.014 0.012 0.01 0.008

ICAC NFCAC NFARC

* f

P

0.006 0.004 0.002 0 0

0.2

0.4

0.6

0.8

1

1.2

λd = 0.5)λ ) new call arrival rate λλ (λ( λ v=λ v = d=0.5*λ

The data source is characterized by a self-similar process and the packets are Pareto distributed, and the model is assumed to be consisted of a number of packet call requests which is geometrically distributed with mean μ N pc = 5 as described in [8].

Figure 4. Forced termination probabilities vs. new call arrival rate Figures 3 and 4 show the outage probabilities and forced termination probabilities versus new call arrival rate for ICAC, NFCAC, and NFARC. The data user generates bursty traffic and it causes the outage probability of type-2 out of order. Since the outage probability of type-2 is out of order, ICAC and NFCAC tend to reject new data call requests and handoff call requests. Thus, ICAC and NFCAC cannot guarantee the QoS requirement of forced termination probability. Fewer data users in the system

The packet call requests are separated by reading time which is geometrically distributed with mean μ D pc = 12 seconds. Each packet call consists of a number of packets which is geometrically distributed with mean μ N p = 25 . The time interval between two consecutive packets is geometrically distributed with mean

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call request on the adjacent cell base stations, the CAC scheme becomes more precise. NFARC reduces the outage probability of adjacent cells, which is caused by accepting a new call request. New call requests, which are located near cell boundary, will be rejected when the system loading of adjacent cell is high. And the handoff call requests will be accepted more. With rate control at each data burst, the mean number of users and mean packets in a cell are improved significantly. According to the huge bursty traffic, ICAC cannot guarantee the outage probability of type-2 and the forced termination probability. To solve this problem, the rate control scheme has to be considered.

will lower the total interference so the outage probability of type1 is reduced. NFARC can overcome this situation by adopting rate control on data users. Data traffic will not be so huge and the QoS requirements can be guaranteed.

mean number of users in a cell

30 25

voiceusers

20 ICAC

15 10

NFCAC

data users

NFARC can guarantee the QoS requirements, maximize the mean number of users in a cell, and maximize the mean packets per second transmitted in a cell. Therefore, NFARC is suitable for WCDMA cellular systems. On the other hand, the data delay time is not a QoS requirement in this paper. If the data delay time constraint is considered, the data delay time should be an input linguistic variable of the CAC scheme.

NFARC

5 0 0

0.2

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new call arrival rate λλ (λ( λv=λ d=0.5*λ )) v =λ d = 0.5λ

Figure 5. Mean number of users vs. new call arrival rate

mean packets per second transmitted in a cell

5. REFERENCES [1] S. M. Shin, C. H. Cho, and D. K. Sung, “Interference-based channel assignment for DS-CDMA cellular systems,” IEEE Trans. Veh. Technol., vol. 48, no. 1, pp. 233-239, Jan. 1999.

1000 900 800

[2] N. Dimitriou and R. Tafazolli, “Quality of service for multimedia CDMA,” IEEE Commun. Mag., vol. 38, no. 7, pp. 88-94, July 2000.

700 600 500 400

[3] S. Shen, C. J. Chang, C. Y. Huang, and Q. Bi, “Intelligent call admission control for wideband CDMA cellular systems,” IEEE Trans. Wireless Commun., vol. 3, no. 5, pp. 1810-1821, Sept. 2004.

ICAC NFCAC

300 200

NFARC

100

[4] K. R. Lo, C. J. Chang, and C. B. Shung, “A neural fuzzy resource manager for hierarchical cellular systems supporting multimedia services,” IEEE Trans. Veh. Technol., vol. 52, no. 5, pp. 1196-1206, Sep. 2003.

0 0

0.2

0.4

0.6

0.8

1

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λv = d=0.5*λ λd = 0.5)λ ) new call arrival rate λλ (λ ( v=λ

Figure 6. Mean packets transmitted vs. new call arrival rate

[5] C. Comaniciu, N. B. Mandayam, D. Famolari, and P. Agrawal, “Wireless access to the world wide web in an integrated CDMA system,” IEEE Trans. Wireless Commun., vol. 2, no. 3, pp. 472-483, May 2003.

Figures 5 and 6 illustrate the mean number of users and mean packets per second transmitted in a cell versus new call arrival rate for ICAC, NFCAC, and NFARC. Notably, ICAC and NFCAC cannot guarantee the QoS requirements and most of the data users are rejected. The utilizations of ICAC and NFCAC are not efficient, and NFARC overcomes this situation. NFARC accepts users more than ICAC by an amount of 45.35%, and transmits more packets than ICAC by an amount of 123.14%.

[6] Y. L. Hsieh, C. J. Chang, and Y. S. Chen, “A power control scheme with link gain prediction using PRNN/ERLS for DSCDMA cellular mobile systems,” Proc. IEEE ICC ‘03, vol. 1, pp. 407-411, May 2003. [7] J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-fuzzy and soft computing, Prentice-Hall, 1997.

4. CONCLUDING REMARKS

[8] UMTS TR 101 112 V3.2.0 (1998-04), “Universal mobile telecommunications system; selection procedures for the choice of radio transmission technologies of the UMTS (UMTS 30.03 version 3.2.0),” European Telecommunications Standards Institute, Tech. Rep., 1998.

This paper proposes a neural fuzzy call admission and rate controller (NFARC) for WCDMA cellular systems to support differentiated QoS provisioning, satisfy the system QoS constraints, and maximize the system utilization. By using the PRNN/ERLS interference predictor, NFARC predicts the system interference at the next time period. It has been shown to achieve significantly higher prediction precision. The neural fuzzy call admission processor considers the system QoS performance measures of each traffic type and the interference of home cell and adjacent cells to determine whether to accept the call request or not. By considering the influence of a

[9] K. S. Gilhousen, I. M. Jacobs, R. Padovani, A. J. Viterbi, L. A. Weaver, Jr., and C. E. Wheatley III, “On the capacity of a cellular CDMA system,” IEEE Trans. Veh. Technol., vol. 40, no. 2, pp. 303-312, May 1991.

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