Hybrid Channel Allocation in Wireless Network

using Evolutionary Strategy

S. R. Shinde1 sandeep.shinde@vit.edu

Dr. G. V. Chowdhary2 girish.chowdhary@gmail.com

M. L. Dhore1 hodcomp@vit.edu

Archana S. Shinde3 jad_arch25@yahoo.co.in

1. Vishwakarma Institute of Technology, Pune, India

2. Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, India

3. Sinhagad Academy of Engineering, Pune, India

Abstract— Radio spectrum is limited resource in wireless mobile

communication system. Cellular system has to serve the

maximum possible number of calls while the number of channels

available is limited. Hence the problem of determining an optimal

allocation of channels to mobile users that minimizes call-

blocking and call-dropping probabilities is of paramount

importance. This paper proposes a hybrid channel allocation

model using an evolutionary strategy with an allocation distance

to give efficient use of frequency spectrum.

Keywords- Cellular network, channel assignment, evolutionary

strategy (ES), radio spectrum.

I. INTRODUCTION

The cellular concept replaced the use of large geographical area with a number of non-overlapping smaller geographic areas called cells. Each cell is with a base station and number of mobile terminals. The mobile terminals within a cell communicate through wireless link with the base station. A number of base stations are connected to Base Station Controller (BSC). A number of BSC are connected to the Mobile Switching Centers (MSC). The radio spectrum is divided into ranges of frequencies (sub-ranges) called channels and each transmitter is required to operate on these channels [1].

The channel assignment is allocating channels to each radio cell in cellular radio network. The channel assignment is NP-hard problem [2]. The role of a channel allocation scheme is to allocate channels to cells in such a way as to minimize the probability that the incoming calls are blocked, the probability that the ongoing calls are dropped. To minimize this call blocking and call dropping probabilities, the channel allocation scheme must satisfy electromagnetic compatibility constraints [3][12] as well as demand of traffic [3]. The electromagnetic compatibility constraints consist of two types soft constraint, hard constraint. The hard constraints are co-channel interference, adjacent channel interference, and co-site interference. The soft constraints are packing condition, resonance, limiting rearrangement.

Channel assignment scheme is classified into three categories, Fixed Channel Assignment (FCA), Dynamic

Channel Assignment (DCA) and Hybrid Channel Assignment (HCA).

FCA allocated channels to each cell permanently. FCA systems typically allocate channels in a manner that maximizes frequency reuse. Thus, in a FCA system, the distance between cells using the same channel is the minimum reuse distance for that system. In DCA, channels are allocated dynamically as call arrives. DCA system has higher degree of randomness but involves complex algorithms. FCA is simpler and outperforms DCA under heavy load conditions, but FCA does not adapt to changing traffic conditions [4]. HCA scheme was proposed by Kahwa et. al. [5], which combines benefits of both FCA and DCA. In HCA one set of channel is allocated as in FCA and other set is allocated as in DCA.

II. RELATED STUDY

Many solutions are proposed in the literature to solve FCA, DCA and HCA problems. This includes Neural Networks [6], Simulated Annealing [7], Genetic Algorithm [12], and Evolutionary methods [8], [9]. While going through this paper you will find it out problem statement in Section III, proposed HCA-scheme in Section IV, Evolutionary Strategy in section V, Cellular Model Assumptions in VI, and Simulation Results in VII.

III. PROBLEM STATEMENT

Channel assignment scheme helps to increase the networks capacity by efficiently distributing channels across the network, In this paper, we study the problem of hybrid channel allocation. Channel assignment is made by the controller of the concern base station according to knowledge about the neighbors of given cell and overlap between the channels. The fitness function takes care of soft constraints. The hard constraints are taken care of by the problem representation and our proposed new scheme. Our scheme is similar to the D-ring HCA scheme as of [8] with exception that the new concept of allocation distance.

When two in-range transmitters operate on the same channel they interfere with each other. Such interference is known as co-channel interference. When two transmitters operates on adjacent channels that partially overlap, they cause

72978-1-4244-4791-6/10/$25.00 c©2010 IEEE

lesser degree of interference, which is referred to as adjacent channel interference.

IV. PROPOSED HCA-SCHEME

We consider a cellular radio system with a finite set of channels and cells. In wireless cellular network, a channel corresponds to up-link and down-link transmission between mobiles and base stations. The up-link (mobile to base frequency) and the down-link (base to mobile frequency) are assumed not to interfere with each other and are allocated in the same manner with the same channel assignment scheme. In this paper we will only consider the down-link frequency allocation.

In this paper our allocation distance is to avoid the adjacent channel interference as two transmitters operates on adjacent channels interference will occur. So the minimum distance between the centers of two channels to be allocated is three, which is a distance between the centers of two channels as shown in Fig. 1. This will overcome the problem adjacent channel interface.

Channels are allocated to host cell from a set of channels which excludes all those channels which are in use in the interference region. As such the selected channels always satisfy the co-channel and adjacent channel interference constraint too.

Let i be the total number of cells in the network and j the total number of channels in the network. The allocation matrix is the binary matrix i*ja such that

ija i*j

cell toassigned is channel ifotherwise

10

{�

The allocation matrix is updated every time a channel is allocated and released in the network.

V. EVOLUTIONARY STRATEGY

Rechenberg [11] invented ES. It was proposed as an optimization method for real-valued vectors. It works on an encoded representation of the solutions. Each candidate solution is associated with an objective value. The objective value is representative of the candidate solution’s performance in relation to the parameter being optimized.

In this section we describe a multi-objective (μ, �) –ES for determining an optimal assignment of channels that minimizes call-blocking and call-dropping probabilities. Our ES maintains a population of μ parent solution and � offspring solution [9]. Each solution is encoded in such a way that appropriate genetic operators can be defined for evolution of the population.

A. Problem Representation

Let us assume that a new call arrives in cell k, which is already serving (dk-1) calls and dk is the number of active channels at cell k after the new call arrives. Our problem is to assign a channel for the new call also with possible re-assignment of channels to the (dk -1) ongoing calls in k, so as to maximize the overall channel usage in the entire network.

Allocation distance is taken care by fitness function and proposed algorithm.

A potential solution, Vk is an assignment of channels to all ongoing calls and the new calls at k. we call such a solution a chromosome. We will represent Vk as an integer vector of length d. where each integer is a channel number being assigned to a call in cell k. for example if k=1, d=4 available channel numbers [1,2,3,4,5,6,7,8,9], then a possible solution is V1= [7,2,5,3].

Figure 1. Allocation Distance between channels

B. Initial parent and Initial population

When a call arrives in a cell k at time t, we first determine the set of eligible channels I that can serve the call. First we search in fixed channels if not found then we search from dynamic channels. Here I(k, t)= set of available channels/ set of channels of ongoing calls in k at time t. This information is obtained from allocation matrix. An initial parent solution (first chromosome) is selected from a set G (initial population) of � solution vector where �= | I(k, t)|. Each solution vector in G is evaluated according to the fitness function and the individual with the best fitness is selected as initial parent. In order to find an optimal combination of channels for the cell k, we preserve in the initial population the (dk -1) channels already allocated to k before arrival of new call. Thus each solution in G contains a unique integer selected from I(k, t) the remaining (dk -1) integers in all solution vectors are the same and are the channels of the ongoing calls in k, that is P(k, t) for instance, let us consider the example : a call arrives in cell k at time t. where P(k,t)= [2,4,5] and available channels are [1,2,3,4,5,6,7,8,9]. Therefore, I(k, t) =[1,3,6,7,8,9] and �=6. Here, dk =4 and hence the size of the solution vector is 4. The six solution vectors in G are thus

G1=[2,4,5,1], G2=[2,4,5,3], G3=[2,4,5,6] G4=[2,4,5,7], G5=[2,4,5,8], G6=[2,4,5,9]

Out of these six candidate solutions the μ� | I (k, t) | best solutions are selected as initial parent.

C. Allocation distance

We proposed a distributed dynamic channel allocation strategy. In this strategy channel assignment is made by controller of the concerned base station, According to the knowledge about the neighbors of a given cell and allocation distance.

Allocation distance shown in Fig. 1. of a system is distance between the centers of channel i and j. Using D-ring [8] selected channels always satisfy the co-channel interference constraints. Allocation distance is used to satisfy the adjacent channel interference constraints. Allocation distance between

2010 IEEE 2nd International Advance Computing Conference 73

the centers of two channels allocating to certain pair of cells is less than three it means there is higher possibility of interference. To avoid that interference the selected channels must be separated by some minimum distance greater than three.

D. Fitness

In this section, we define a fitness function that express objectives of HCA with allocation distance. In this case we focus on co-channels, adjacent channel interference constraints. The hard and soft constraints [13] can be modeled as an energy function. The minimization of this energy function gives an optimal channel allocation.

jkk

j

A

i

j

C

kii

j

C

ii

yxAllocationE

,V

k

k

jVk,i,

k

jVk,i,

,d

1

M3-

(1) k)),res(-(1 d

1 ,1

A M2

d

1

k1,

),(.AM1-

�

�

�

�

�

��

�

�

�

�

��

�

where

k : Cell where a call arrives

dk : Number of channels allocated to cell k

The proposed algorithm starts with an initial parent generated. At every generation the size of population is �. These � individuals of the new population are randomly generated from the actual parent by the process of mutation. The fittest individual from the newly generated population form the parent for the next generation. The fitness of the best individual child is better than the former parents now it becomes the parent. The best solution is updated whenever its fitness is worse than that of the local best solution. The algorithm terminates when it will get desired solution or a termination condition occurs. During this process one of the three possibilities is selected with probability 1/3 and exactly N mutation. When a call arrives, system looks for channels which are not in use in the cell and its neighboring area. If no such a channel found the call is blocked, otherwise ES algorithm finds a solution. The steps of algorithm is enlist as follows

(traffic demand in cell k)

C : Number of cells in the network

Vk : Output vector (the solution) for cell k with

dimension dk

jVk, : jth element of vector Vk

jVk,i,A : element located at the ith row and jVk,th

column of the allocation matrix A

res(i,k) : function that returns a value of one if the

cells i and k belongs to same reuse scheme,

otherwise zero.

x : channel allocated to cell i

y : is the channel equals to jVk,

Allocation(x,y): Function that returns a value of one if distance between channel x and y is greater than 3, otherwise zero.

The first term expresses the packing condition. If the jth element of vector Vk is also in use in cell i, and the cell i and k are free from co-channel interference then the energy decreases. The decrease in energy depends on the distance between the channels x and y. The second term determines the resonance condition. The energy increases if the jth element of vector Vk is also in use in cell i, and cells i and j does not

belong to the same allocation distance. The last term expresses the limiting reassignment [8].

E. Mutation

An offspring is generated from a parent by flipping channels of the parent with channels from the set of free channels the number of flips is random and between 1and N. The parameter N=min(dk, | I |) is the maximum number of flips.

F. ES Approach

Algorithm

Begin

Create initial population of � individuals

Find out the fitness of each individual

Select the best individual as parent

Repeat

Generate � neighbors of parent by mutation

Find out the fitness of each individual

Select the best individual as best-child

count = 0

Do

parent = best-child

mutate new parents

best-child = new fittest individual in

population

count = count+1

while ((best-child>parent) and count<10)

if (channels-in-use< free-channels)

N = channels-in-use

Else

N = free-channels

Until termination criteria will reach

End.

74 2010 IEEE 2nd International Advance Computing Conference

Figure 2. Wireless Network Model

VI. CELLULAR MODEL ASSUMPTIONS

In this paper, ES is applied to the mobile cellular model used in [8]. The channel assignment assumptions are briefly described as follows.

� The topological model is a group of hexagonal cells that form a parallelogram shape as shown in Fig. 2 (from [10], Fig. 1).

� Cells are grouped in a cluster of size 7 cells. The reuse distance is 3 cell units as shown in Fig. 3 (from [10], Fig 9).

� A total of 70 channels are available to the whole network. Each channel may serve only one call.

� A call is blocked if the entire set of channels in the network is in use in the cell involved in call arrival and its neighborhood, that is there is no channel that satisfies the co-channel interference constraint.

� Incoming calls at each cell may be served by any of the system channels.

In the model, an incoming call is served immediately if a channel is available, otherwise the call is blocked and there is no queuing of blocked calls. The special characteristics of this model is infinite number of users, finite number of available channels, memory less arrival of requests, call arrival follows the non uniform traffic as proposed in [9] and shown in Fig. 4., call duration is random. With this simulation we can compare our results with those obtained in [8].

Figure 3. Reuse distance used in the model

Figure 4. Non Uniform traffic distribution pattern.

VII. SIMULATION

In HCA, total set of channels is divided into two sets: static set and dynamic set. When a call arrives in randomly selected cell, the cellular system first makes an attempt to serve it from the static set of channels. When all the channels in the fixed set of channels are busy, the cellular system applies ES algorithm to find a suitable combination of channels. In this paper representative ratios proposed in [9] were used: 21:49 (21 channels in static set and 41 channels in the dynamic set) 35:35, and 49:21. Results were obtained by increasing the traffic rate with respect to the initial traffic rate.

2010 IEEE 2nd International Advance Computing Conference 75

0

0.05

0.1

0.15

0.2

0.25

0 20 40 60 80 100 120 140

Percentage increase of traffic load

Blo

ckin

g p

rob

ab

ilit

y

D-ring HCA

Proposed HCA

Figure 5. Performance of the proposed ES algorithm in terms of blocking

probability, for FCA = 21and DCA = 49

0

0.05

0.1

0.15

0.2

0.25

0 20 40 60 80 100 120 140

Percentage increase of traffic load

Blo

ck

ing

pro

ba

bil

ity

D-ring HCA

Proposed HCA

Figure 6. Performance of the proposed ES algorithm in terms of blocking

probability, for FCA = 35 and DCA = 35

The performance of proposed ES based algorithm for channel allocation has been derived in terms of call blocking probability for the new incoming calls. This blocking probability is the ratio between the new call blocked and the total call arrivals in the system. The values of the positive constants are set to M1= 1.5, M2=0.5, M3=1, same as [8]. We have tested the performance of the algorithm for any values of � and not limited to any particular values of � as in [8]. According to our simulations, the proposed algorithm produces better results in comparison with Vidyarthi et. al. [8]. The convergence of the proposed ES is shown in Table I.

VIII. CONCLUSIONS AND FUTURE DIRECTION

We proposed an evolutionary strategy that efficiently combines the objectives of both hybrid channel assignment with allocation distance in order to increase the capacity of wireless mobile network and reduce the wastage of available spectrum. More research is required to further reduce the wastage of available spectrum and to reduce the call blocking and call dropping probabilities.

0

0.05

0.1

0.15

0.2

0.25

0.3

0 20 40 60 80 100 120 140

percentage increase of traffic load

Blo

ckin

g p

rob

ab

ilit

y

D-ring HCA

Proposed HCA

Figure 7. Performance of the proposed ES algorithm in terms of blocking

probability, for FCA = 49 and DCA = 21

TABLE I. CONVERGENCE OF PROPOSED ES

% Increase in

Traffic load

Average blocking

probabilities

0 0.000

20 0.000

40 0.000

60 0.000

80 0.033

100 0.043

120 0.083

FCA 21, DCA

49

140 0.121

0 0.000

20 0.000

40 0.000

60 0.000

80 0.025

100 0.113

120 0.147

FCA 35, DCA

35

140 0.179

0 0.000

20 0.000

40 0.004

60 0.050

80 0.083

100 0.117

120 0.161

FCA 49, DCA

21

140 0.210

REFERENCES

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[2] W. K. Hale, “Frequency Assignment: Theory and Applic- ations”, Proc. IEEE, vol. 68, no. 12, pp. 1497–1514, 1980.

[3] Sandip R. Shinde, M. L. Dhore, J.B. Karande, “Avoiding interferences in WLAN 802.11b for partially overlapped channels,” Proceedings of

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the ACM International Conference on Advances in Computing, Communication and Control, pp. 703–706, 2009.

[4] W. K. Lai and G. C. Coghill, “Channel Assignment thro- ugh Evolutionary Optimization,” IEEE Transactions on Vehicular Technology, vol. 45, no. 1, pp. 91–96, 1996.

[5] T. J. Kahwa and N. D. Georgans, “A Hybrid Channel Assignment Schemes in Large-Scale, Cellular Structured Mobile Communication Systems”, IEEE Transactions on Communications, vol. 26, pp. 432–438, 1978.

[6] Nobuo Funabiki and Yoshiyasu Takefuji, “A Neural Network Parallel Algorithm for Channel Assignment Problems in Cellular Radio Networks”, IEEE Transacti- ons on Vehicular Technology, vol. 41, no. 4, Nov. 1992.

[7] M. Duque-Anton, D. Kunz, and B. Ruber, “Channel assi- gnment for cellular radio using simulated annealing”, IEEE Transactions on Vehicular Technology.,vol. 42, no. 1, pp. 14-21, Feb. 1993.

[8] G. D. Vidyarthi, A. Ngom, and Ivan Stojmenovic, “A Hybrid Channel Assignment Approach using an Efficient Evolutionary Strategy in Wireless Mobile Networks”, IEEE Transactions on Vehicular Technology, vol. 54, no. 5, pp. 1887–1895, 2005.

[9] H. G. Sandalidis, P. Stavroulakis, and J. Rodriguez- Tellez, “An Efficient Evolutionary Algorithm for Channel Resource Management in Cellular Mobile Systems”, IEEE Transactions on Evolutionary Computation, vol. 2, no. 4, pp. 125-137, 1998.

[10] Enrico Del Re, Romano Fantacci, Luca Ronga,“A Dynamic Channel Allocation Technique Based on Hopfield Neural Networks”, IEEE Transactions on Vehicular Technology, vol. 45, no. 1, Feb. 1996.

[11] I. Rechenberg, Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Stuttgart, Germany: Frommann-Holzboog Verlag, 1973.

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Sandip Shinde received B.E. degree in Computer Science and Engineering from Dr. Babasaheb Ambedkar Marathwada University, Maharashtra, India, in 2002, M.Tech degree in Computer Engineering from Dr. Babasaheb Ambedkar Technological University, Maharashtra, India, in 2008.

He is currently with Vishwakarma Institute of Techology, Pune, Maharashtra, India, as an Assistant Professor in Computer Engineering Department. His research includes Wireless Mobile Computing, Optical Network, Wireless Sensor Network, Applications of Evolutionary Computation.

Girish Chowdhary received B.E. degree in Computer Science and Engineering from Marathwada University, Maharashtra, India, in 1991, M.E. degree in Computer Science and Engineering from BITS Pillani, Rajasthan, India in 1994, and PhD from IIT Madras in 2007.

He joined the department of Computer Engineering Dr. Babasaheb Ambedkar Technological University, Lonere, Maharashtra, India in 1994. At present he is working as Professor and Head. His research includes WDM Optical Network, Wireless Mobile Computing, and Network Security.

M. L. Dhore received B.E. degree in Computer Engineering from Amravati University Maharashtra, India, in 1989, M.E. degree in Computer Science and Engineering, from Thapar Institute of Engineering and Technology, Panjab, India in 1998.

He is currently with Vishwakarma Institute of Techology, Pune, Maharashtra, India, as an Assistant Professor and Head of Computer Engineering Department. His research includes Localization of languages, Wireless Mobile Computing, Network Security.

Archana Shinde received B. E. degree in Computer Science and Engineering from Swami Ramanand Marathwada University, Maharashtra, India in 2003.

She is currently with Sinhagad Academy of Engineering, Pune, India as Lecturer in Information Technology Department. Her research includes Distributed Computing, Wireless Mobile Computing.

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