Joint Radio Resource Management for HSDPA and WiMAX Networks

Hesham El Sawy

Network Planning Department NTI

Cairo, Egypt elsawyhesham@hotmail.com

Hesham El Badawy Network Planning Department

NTI Cairo, Egypt

hesham@nti.sci.eg

Khaled A.Shehata Electronics and Communication Dep.

AAST Cairo, Egypt

K_Shehata@aast.edu

Abstract— A vision of future wireless networks is the coexistence of multiple access network technologies. Different access networks will coexist and the Internet Protocol will be the joint layer. Controlling the resources of these networks is very challenging. This paper deals with two data oriented access networks, the worldwide interoperability for microwave access (WiMAX) and the high speed downlink packet access (HSDPA). A new scheduling algorithm to work with large FTP file downloads is suggested, then a queueing model is analytically developed; this model can be used in developing an efficient joint radio resource management (JRRM) protocol to be used by the joint admission controller that controls the access to the two networks. The developed model can also be used to obtain many important performance measurements such as blocking probability, mean sojourn time and the optimum loads of the two networks.

Keywords- FTP; HSDPA; JRRM; Scheduling algorithm; WiMAX.

I. INTRODUCTION Wireless heterogeneous network [1], [2] is a one large

homogenous network composed of different wireless access networks, these access networks use the internet protocol as their core network. These different wireless access networks have different designs, tradeoffs, Data rates, QoS and capacities. To fulfill this vision, one of the greatest challenges is to develop a sophisticated protocol that utilizes the usage of all these networks together. When and in what way the serving network is chosen affects the performance of the mobile services. The network selection algorithm may be based on the user application constrains, mobility profile and operator policies. In [3], a network selection algorithm was analytically developed based on throughput maximization for HSDPA and WiMAX cooperation. Whereas in [4], the developed algorithm was based on user preferences and service application for WLAN and UMTS cooperation.

In this paper, the presented model analyzes the cooperation of two data oriented networks, which are the WiMAX and the HSDPA, and a novel scheduling algorithm is suggested for FTP download like sessions. It is assumed that each user intends to download a file with exponentially

distributed size of mean γ, and the user will stay at the network until the proposed file is fully downloaded.

Theoretically, the two systems can serve infinite number of data sessions, but this will result in a very significant drop in the effective throughput for each user, which will result in a very large sojourn time and system congestion. To avoid this problem, the capacity of the WiMAX and HSDPA systems is limited to Mw and MH respectively, and this will ensure reasonable throughput per user at any time. Then the optimum number of simultaneously served users will be investigated to determine the system capacity.

Upon a data session request, the JRRM protocol attaches the user to the network offering higher throughput and the user remains there until the end of his download, and upon a new download request the JRRM will repeat previous task. The JRRM will block any download session attempt after reaching the maximum capacity limit.

The two networks implement adaptive modulation and coding (AMC) techniques. This means that users with poor channel condition will suffer poor data rate, leading to long download time and network congestion. The proposed JRRM model maximizes the data rate, which means that it assigns the user to the network having better channel conditions. This guarantees acceptable throughput and acceptable download time.

The remainder of the paper is organized as follows. In section II and III, the WiMAX and HSDPA models for throughput calculations are presented respectively. In section IV, the proposed JRRM protocol is presented. The numerical results are shown in section V. The paper is concluded in section VI.

II. WIMAX NETWORK MODEL In this section, the mean throughput per user and the

departure rate for WiMAX network will be calculated analytically.

The resources in WiMAX are shared by users in both frequency and time. In the suggested scheduling algorithm, instead of dividing the entire BW into subchannels, which is assigned to each user upon his arrival as in [3], the base station gives the first user the entire BW, and upon the

2009 First International Conference on Computational Intelligence, Communication Systems and Networks

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DOI 10.1109/CICSYN.2009.6

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arrival of a new user the BW is equally shared between all the served users. After reaching the maximum capacity of MW, any new request will be directed to the other network or will be blocked. Since that as the throughput increases the download time decreases and the departure rate increases, then this scheduling technique utilizes the BW and increases the effective throughput per user at small system loads, subsequently increasing the overall system throughput, avoiding rapid network congestion and decreasing the overall sojourn time.

In WiMAX [5], data are mapped to subcarriers, each subcarrier can carry one mapped symbol per transmitted OFDM symbol. Then the OFDM symbols are concatenated together to form a frame. Let the number of bits per OFDM symbol (Nbpo), the number of OFDM symbols per frame (NopF) and the number of frames per second (NFps) must be calculated to determine the data rate (Rw).

FpSopFbpo NNNRw (1)

The OFDM symbol duration TOFDM will be calculated as in [6] to compute the NopF. If Fs is the sampling frequency, Δf is the subcarrier spacing, Tb is the OFDM symbol duration without cyclic prefix, Tg is the cyclic prefix duration (i.e. guard time), TF is the frame duration, Nfft is the FFT size, n is the oversampling rate and BW is the channel bandwidth, then from [6]:

8000)8000

(1 BW

nFloor

NGT fft

OFDM

(2)

So the total number of OFDM symbols per frame is:

8000)8000

(1

BWnFloor

NG

TT

TN

fft

F

OFDM

Fops

(3)

If the overhead time percent used for the frame guard time (i.e. inter frame space) is Toh, NDL is number of Downlink frames in each duplex process and NT is the total number of frames in each duplex process, then the number of frames per second is:

T

DLoh

FFps N

NT

TN )1(

1 (4)

And the number of bits per OFDM symbol is: )(log2 MRNN cdatabpo (5)

Where Ndata is the number of data subcarriers, Rc is the code rate, and M is the modulation order. So the effective throughput can be calculated by substituting in (1) from (3), (4), (5).

T

DL

fft

ohcdataw

NN

BWnFloor

NG

TMRNR

8000)8000

(1

)1()(log2 (6)

If nw is the number of users served by the WiMAX network, then the throughput per use is:

w

www

usernRnR )( (7)

And the departure rate is:

w

www

nRn 1)( (8)

III. HSDPA NETWORK MODEL In this section, the mean throughput per user and the

departure rate for the HSDPA network are analytically calculated.

The allowed physical resources “i.e. codes” in HSPDA [7], [8] are shared by users in both codes and time. The same scheduling algorithm used in the WiMAX network will be used, and the HSDPA network capacity will be limited to MH users. Also since that as the throughput increases, the download time decreases and the departure rate increases, then this scheduling technique utilizes the BW and increases the effective throughput per user at small system loads, subsequently increasing the overall system throughput, avoiding rapid network congestion and decreasing the overall sojourn time.

Let W is the WCDMA chip rate which is 3.84Mbps, and SF is the spreading factor which is equal to 16 in case of HSDPA channel. Then the symbol rate can by calculated by:

SFWRsymbol

(9)

The bit rate can by calculated by multiplying the symbol rate by the number of bits per symbol, where the number of bits per symbol is determined by the modulation order (M), then the number of bits per symbol is log2(M). In order to determine the useful data rate, the redundant bits due to FEC coding must be removed by multiplying by the coding rate Rc.

SFRMW

R ceff

)(log2 (10)

The previous result is obtained assuming that the transmission is error free and no hybrid automatic repeat request (HARQ) retransmission is involved. In actual system, the frame error probability and the HARQ retransmissions have to be taken into consideration, then the effective data rate will tend to:

HARQ

ceff NSF

FERRMWR

)1()(log2 (11)

Where NHARQ is the mean number of HARQ retransmissions, FER is the frame error rate. NHARQ and FER will be computed as described in [9].

In HSDPA, simultaneous Walsh codes can be used to increase the data rate, therefore the effective data rate is:

codesHARQ

cH NNSF

FERRMWR

)1()(log2 (12)

191

Then if nH is the number of users served by the HSDPA network, then the throughput per use is:

H

HHH

usernRnR )( (13)

Where Ncodes are the number of simultaneously used Walsh codes. Hence, the departure rate is:

H

HHH

nRn 1)( (14)

IV. PROPOSED JRRM PROTOCOL In this section, a Markovian model for the downlink

traffic of the cooperative WiMAX and HSDPA networks will be developed.

As discussed in the previous sections, upon the arrival of any file download request, the JRRM protocol will compute the expected throughput offered by both networks and will assign the user to the network offering the higher throughput. The expected throughput for WiMAX network:

1)1( w

www

usern

RnR (15)

The expected throughput for HSDPA network:

1)1( H

HHH

usern

RnR (16)

So, if the global user’s arrival rate is λ user/sec, then the arrival rate in the WiMAX network is only evaluated if nw <Mw and is equal to:

OtherwisenRnRif

nnHH

userww

userwHw

;0)1()1(;

),( (17)

And the arrival rate in the HSDPA network is only evaluated if nH <MH and is equal to:

OtherwiseMnif

nnHHw

wHH

;0;

),( (18)

In the proposed model, upon the arrival of a new user (in WiMAX or HSDPA networks), the user will equally share the resources with the existing ones; this will of course affect the departure rate at the new state.

Since the two networks capacity is finite (assuming that Mw = MH = C users), the arrival and departure rates depend on the number of served users per system and only one step transition is permitted at a time, this results in a finite level dependant quasi-birth-death (LDQBD) process, LDQBD process is a two dimensional queueing system where the arrival and departure probabilities depend on the current level. LDQBD process can be solved using the matrix geometric method (MGM) [10], where MGM is a method used to obtain the state probability of each level. The infinitesimal generator matrix Q can be estimated from the state diagram shown in Fig. 1.

Figure 1. LDQBDP model.

In [10], MGM was introduced to solve homogeneous (level independent QBD process), while in this paper the MGM will be used to solve a finite LDQBD process due to the finite capacity and the departure rate dependency on the number of served users in each network. The proposed methodology is a combination of the models introduced in [3], [10], [11] and [12] to apply the MGM in solving a finite LDQBD process for a cooperative WiMAX and HSPDA networks.

12

10

12

20

42

31

32

20

21

22

10

11

12

000

0000

00000000

00000000

BAAA

AAAAAAA

AAAAB

Q

C

CC

C (19)

Where B0, B1, iA0 , iA1 , iA2 are square matrices of size C,

and the submatrix iA0 is a diagonal matrix (which corresponds to the HSDPA session request) with values equal to λH(nH,nw) as in (20), and the submatrix iA2 is a diagonal matrix (which corresponds to the HSDPA session termination) with values equal to μw(nH) as in (21), while the matrix iA1 is a tri-diagonal with structure as in (22).

OtherwiselmifmllmA

Hi

;0;),(),(0 (20)

192

OtherwiselmifmllmA

Hi

;0;),(),(2

(21)

Otherwiselmifmlmifmi

lmifmi

lmA w

w

WH

i

;01;)(1;),(

;))()((

),(1

(22)

The two sub-matrices B0, B1 are just like iA1 but with the proper modifications to match the boundary levels (level 0 & level 1) respectively.

The elements of the generator matrix Q are graphically arranged and indexed to be grouped into finite submatrices, where as the levels will denote the HSDPA network states, and the phases within each level will denote the WiMAX network states. To evaluate the performance of the proposed JRRM algorithm, the model will be solved by means of MGM to obtain the unique steady state probability matrix.

][ 543210 HM (23)

Where the vectors πi contain the probabilities of being in one of the WiMAX phases within a HSDPA levels i:

)],()3,()2,()1,()0,([ wi Miiiii (24)

It can be shown that the model is an ergodic Markov chain [9], then the unique steady-state probability matrix π of that LDQBD model exists, and can be found by solving the set of equations:

Q (25)

Along with the normalization condition (e is being the vector of ones of length C2):

1e (26)

To solve these two equations and find the steady-state probability vector π for the finite LDQBD model, the rate matrices [11] of the corresponding levels (from 0 to

1c ) have to be calculated, and then calculating recursively the state probabilities π in terms of π0 by using the formula:

1

00 .,,2,1;

j

kkj Cj (27)

While computing the rate matrices as follows:

0;)(11;)(

1;

1221

11

00

1221

110

11

10

jatAAACjatAAA

CjatBAj

jjj

C

j

(28)

The vector π0 is then determined by solving the boundary equation:

012100 AB (29)

0)( 12000 AB (30)

Along with the equation (this equation is from substituting in the normalization condition (26) by (27)):

10 0

0 eC

j

j

ii (31)

V. RESULTS AND ANALYSIS This section, presents a comparison between the proposed

cooperative JRRM protocol and the previously published results in [3]. Hence, a comparison between the proposed cooperative JRRM protocol and the individual networks will be illustrated. Then the effect of arrival rates, network capacity and mean file size on the proposed JRRM will be discussed.

Fig. 2 shows a performance comparison between the proposed JRRM protocol and the results published in [3]. As illustrated before, the proposed JRRM protocol aims to utilize the network resources at small and moderate network loads, these lead to avoid rapid network congestion and increase the overall system efficiency. In [3] upon the arrival of each user, the call admission controller allocates the user a variable portion of the available spectrum depending on the number of served users. So there is a portion of the resources will not be utilized at small and moderate network loads, which leads to rapid network congestion.

Figure 2. Blocking probability Vs. arrival rate for the proposed JRRM protocol and the results published in [3].

So Fig. 2 shows that the presented JRRM is

monotonically the same as the previously published in [3]. But there is a significant performance improvement in the presented model due to the deployment of the suggested scheduling algorithm.

Results published in [3]

Proposed JRRM Protocol

193

The rest of this section will discuss the effect of having different arrival rates with the obtained performance measures. The parameters are chosen to be in consistence with [5], [6], [7], and [8]. For the WiMAX network: n=8/7, BW=3.5MHZ, NFFT =256 (802.16d), G=1/32, TF=0.005, Tdd=3/4, Toh=0.125, Ndata=192 [5], [6]. And for the HSDPA network: SF=16, W=3.84 chips/sec, and 12 orthogonal codes of the Walsh tree will be used for HS-DSCH [7]. Also, The HSDPA and WiMAX networks capacities (MH&Mw) will be limited to C=10 users.

Fig. 3 shows the blocking probability for the individual WiMAX and HSDPA networks along with the blocking probability of the proposed JRRM protocol at different arrival rates. The graph shows that at small and moderate arrival rates, the proposed JRRM protocol works with much more better performance. But at very high arrival rate, the system becomes congested and the JRRM protocol performance decreases and tends to be the same as each of the individual technology performance.

Fig. 4 shows the impact of different file sizes on the proposed JRRM protocol.

Fig. 5 shows the impact of network capacities (maximum number of simultaneously served users per network) on the proposed JRRM protocol. The shown graph illustrates that at low system capacity the resources are not fully utilized, which increases the blocking probability. At high system capacity the networks become congested and the blocking probability increases again.

Figure 3. Blocking probability Vs. arrival rate at C=10, γ = 4Mb.

Figure 4. Blocking probability Vs. arrival rate for different file

sizes and C=10.

Figure 5. Blocking probability Vs maximum number of simultaneous served users per network for different arrival rates

ant at γ =4Mb.

Fig. 6 shows the impact of network capacities (where C is the capacity per network) on file mean download time (Sojourn time) for the proposed JRRM protocol. The shown graph illustrates that small mean download times are obtained at low system capacity, and as system capacity increases, the mean download time increases, which leads to network congestion.

194

Figure 6. Mean download time Vs arrival rate for different network capacities and at γ=4Mb.

VI. CONCLUSION An analytical model for JRRM protocol for cooperative

WiMAX and HSDPA has been presented. The proposed scheduling algorithm fits the FTP file download application and enhances the overall system performance.

The model can be used in the current phase of heterogeneous wireless networks, if the user terminal has interfaces capable of operating with the WiMAX and HSDPA technologies.

The obtained results show that the JRRM protocol utilizes the network resources at small and moderate network loads, hence decreases the probability of congestion and blocking probability. Also the impact of the network capacity shows that at low capacity the resources are not fully utilized and hence decreases the network performance. As the capacity increases the performance becomes better. But at high network capacity, the networks become congested again which results in a very poor network performance.

The used MGM algorithm can be used in solving any two dimensional queueing model that describes the cooperation of any two other networks.

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