Dynamic Characterized Genetic Algorithm forAdaptive Beam Forning in WCDMA System

Tiong Sieh Kiong1, Mahamod Ismail2, Azmi Hassan3

'College ofEngineering, Universiti Tenaga Malaysia (UNITEN)km 7, Kajang-Puchong Road, 43009 Kajang, Selangor Darul Ehsan, Malaysia, siehkiong( uniten.edu.my

2Faculty ofEngineering, Universiti Kebangsaan Malaysia (UKM)43600 UKM Bangi, Selangor Darul Ehsan, Malaysia, mahamod(eng.ukm.mY

3Faculty of Engineering, Universiti Kebangsaan Malaysia (UKM)43600 UKM Bangi, Selangor Darul Ehsan, Malaysia, azmi()vlsi.eng.ukm..mv

Abstract - In this paper, a modified Genetic Algorithmnamed as Dynamic Characterized Genetic Algorithm(DCGA) is proposed. The new DCGA is then integratedinto adaptive beam forming technique to reduce thepower usage of adaptive antenna at WCDMA basestation (Node B) for serving instantaneous mobile needs.As contradict to conventional GA, DCGA is able toadapt itself in chromosome representation and numberof crossover points in order to reduce unnecessarysearching space and thus, increase convergence rateefficiently. Power usage at Node B is used as fitnessfunction to compare the performance ofDCGA and GA.Simulation result has shown that DCGA converges fasterand is superior in adaptive beam forming in the aspect ofpower usage at Node B as compared to conventional GA.

Keywords - Adaptive beam forming, Dynamic CharacterizedGenetic Algorithm, WCDMA

I. INTRODUCTION

One of the major techniques to increase mobilecommunication system capacity is implementing adaptiveantenna into the system. Adaptive antenna beam is capableof separating signals transmitted in the same frequency bandprovided the signals are separated by spatial domain [1],[21.The adaptive beam forming process appropriately combinesthe signals received by the different antenna elements of anantenna array in order to form single output. There are manyalgorithms exist for adaptive beam fonning and recentlymuch attention was drown to algorithm with geneticalgorithm (GA) [3],[4].

weight optimization may get trapped in local optima andresulting in a suboptimum beamforming performance [5],Consequently, GA which is good for heuristic optimizationis adapted for beamforming weight optimization problem.The GA assisted beamforming process requires a fitnessfunction to evaluate the fitness of each individualthroughout the evolutionary optimization. In this paper, afitness function based on power usage of antenna system atNode B is developed for GA evaluation.

1I. ADAPTIVE ANTENNA CONFIGURATION

In this paper, it is assumed that a linear circular array of Lelements is placed at the far field ofM uncorrelated pointsources and the elements are separated by a distance of d =)J2, where A is the wavelength of the sources. Thus, the GAis employed to optimize a set of L complex array weightsbased on the fitness function. A snapshot of simulation isobtained to be presented in this paper, where there are Sactive unit equipments (UE) in a WCDMA cell in theparticular simulation snapshot.

In the simulation, it is assumed that the configurations ofadaptive antenna is a 9-array antenna (L = 9) which is placedin circular. Each antenna array can cover an area of 40degree angle of its own and also coverage the areas ofneighbouring arrays, as shown in Table 1. It is also assumedthat the UEs distribution at the instantaneous moment is asshown in Table 2. Besides, Table 2 shows also the possibleantenna arrays that can be used to serve the UEs in thatparticular simulation snapshot.

The optimization of the antenna array is typically subject toa range of constraints and the classic methods of array

The authors acknowledge that this research is a part of project fundedby government of Malaysia with IRPA 04-02-02-0062-EA289

1-4244-0000-7/05/$20.00 02005 IEEE. 219

Table 1. Adaptive Antenna Beam Coverage

Smart Coverage MaximumAntenna Angle CoverageElement # (Degree) (Degree)

0 0o0°400 32008001 400-80° 0°-120°2 800-1200 40o-160o3 1 20o-1600 800o2Q0°4 1600-2000 120-24005 2000-2400 1600-28006 2400°2800 2000°32007 2400°3200 2400°-36008 3200o3600 3200°-400

Table 2. UE's Angle of Arrival

UE# AOA Possible Used AntennaUEO 81° #l, #2, #3UEI 950 #1, #2 #3UE2 100° #1 ,#2, #3IJE3 107 #1, #2, #3UE4 1200 1, #2, #3, #4UE5 1240 #2,f#3,#4UE6 1340 #2,f#3, #4UE7 1450 #2, #3, #4

IH. CONVENTIONAL GA AND DYNAMICCHARACTERIZED GA

Real parameter coding is applied in this research torepresent the weights (starting and ending angle) of antennabeam. 16 memory bits are used to represent each weightsolution.

(a) Conventional GAIn general, GA based optimization has to be able to performsix basic tasks [6];1. Encode the potential array solutions with the aids of the

genes of the GA,2. Create a string of GA's genes for the sake of forming a

chromosome,3. Create an initial population of GA-based solutions,4. Evaluate the assign fitness values to individuals in the

population of the GA,5. Perform reproduction with the aid of fitness-

proportionate selection of individuals in the population

for creating the next generation of better average fitnessindividuals,

6. Perform mutation of the individuals of the newgeneration by slightly perturbing the individuals for thesake of promoting a higher diversity of solutions in aneffort to avoid getting trapped in local optima.

In the conventional GA, representation of the weights of all9 elements adaptive antenna arrays is carried out at all times.Thus, a total of 144 memory bits representation is used forany solution optimization.

(b) Dynamic Characterized GA

There are 3 new implementations in DCGA; DynamicChromosomes Representation, Match Guided Selection(MGS) for crossover selection and Incremental DynamicCrossover Points (IDCP) for number of crossover points incrossover process.

1. Dynamic Chromosomes Representation

In DCGA, the length of chromosomes that representingarrays weights (solutions) are not fixed. It is dynamicallychanged from time to time based on the complexity of theoptimization problem. An additional process is incorporatedto determine all antenna arrays that are possible to be usedto serve all UEs at a particular moment. The weights ofantenna arrays that are not possible to be used are droppedfrom the chromosomes representation. By referring to Table2, a particular snapshot of simaulation only needs to use 4antenna arrays to serve all the UEs. As thus, DCGA willmanipulate on a total of 64 bits chromosomes for arrayweights optimization.

2. Match Guided Selection (MGS)

Crossover process is the major process in GA operation.Thus, in order to improve the efficiency of solutionsearching, attention was drawn to the crossover selection.Figure 1 shows the process of new proposed crossoverselection method, Match Guided Selection method (MGS). 2storage pools (best storage pool and nonnal storage pool)are utilized under MGS method. Chromosomes are kept inthe pools separately after random generation. The beststorage pool is used to keep a fixed number of bestchromosomes, meanwhile normal storage pool keeps therest of the chromosomes. Roulette Wheel selection is thenperformed to select an individual from normal pool to bematched with an individual from the best storage pool.Roulette Wheel selection method is not applied to the beststorage pool. All individuals in the best storage pool willtake turn to be matched with individuals from normal pool.This method is develop due to the belief that parentchromosomes carrying better fitness values will more likelyto produce better offspring after crossover compared toparent chromosomes with average fitness values.

220

Fig. I Match Guided Selection Process

3. Incremental Dynamic Crossover Points (IDCP)

Another modified point in DCGA is the crossover operator.Literature has stated that real-parameter crossover operatorsare able to produce exploration or exploitation depending on

the way in which they handle the current diversity ofpopulation [7]. Exploration will generate additional diversitystarting from the current chromosomes and exploitation is tocreate better elements from the diversity [8]. Thus, an

adaptive crossover operator namely Incremental DynamicCrossover Points (IDCP) was proposed in this paper. Ascontradict to conventional GA where the number ofcrossover points is fixed throughout the crossover process,number of crossover points in IDCP is dynamically changedwith fitness function value to obtain the benefit of exploringat the early generations and exploiting at the lattergenerations. The number of crossover points at certaingeneration depends on the ratio of fitness value with initialfitness value as shown in Equation 1.

Crossover point, Y= f(9gB) xf(g0)

Where Y,,,x is the maximum crossover points allowed, f(gB))is the best fitness value obtained, f(go) is the initial fitnessvalue and q is the IDCP factor. Through simulation, It isfound that I = 0.2 gives the best simulation result.

(c) Fitness Function

Fitness function developed to evaluate the fitness ofindividual in the aspect of power usage at Node B is shownin Equation 2.

L M M

E)= nZUim +E(Wm +A. +&Pm) (2)I m m

where,

i= Oiif on <UBUEi

<

j n,mnl : if (0mmin<n EI uUE. > °mmax

[m=1 if i 0m,mm < PWm

=_ 0 if Om,maxtm,mn >P

where Om.m> dan 'Mmin are maximum coverage angle andminimum coverage angle for smart antenna element #m andp ialah minimum separation angle between antenna beam.

= I if (((Wm,min U (Om,max <(m1)M)) U

Am 4 (((om,min U (tm,max) > (m + 1)6 )

if (m -1).5<(Omn U ommax)m (m+ 1)6

where - 360/M and M is number of smart antennaelements and;

E1mPm,Wmx 10025

Where pm 5s poynting power transmitted, s is the weightfactor for pm (- =0.001 in this paper), wm is solid angle ofantenna beam. It is assumed that the cable loss at antennaelement is 2.5dB.

111. SIMULATION PARAMETERS

Table 3 shows the main simulation parameters used inconventional GA and DCGA.

Table 3: GA and DCGA Simulation Parameters

GA Simulation Parameter ValueMaximum Generations 5000

Length of Chromosomnes 144 bits (GA)Varying (DCGA)

Population, po 100

Selection Method Roulette Wheel (GA)MGS (DCGA)

Crossover rate, Pc 0.90Mutation rate, Pm 0.05Mutation point, mp INumber of best Chromosomes 3Number of Simulation I 10Power factor of IDCP, ri 0.2 (DCGA)

221

Best chromosomes fromstorage pool are matchedwith chromosomes from

P1 Roulette wheel selection RI

3 best chromosomes arekept in storage pool 4 chromosome:

random generalRoulette Whee

| IR2 RI R3| Crossover process

implementing IDCP

New L NP1 NP2 L 3|Generation NR N|RI NR3

IV. SIMULATION RESULT

Simulation results are shown in Figure 2 to 6. Figure 2shows the memory allocation used by conventional GA andDCGA for the simulation snapshot. Memory bits in circleare the memory bits for representation of one chromosome.In Figure 2(a), it can be seen that conventional GA usesmore memory bits in representing a chromosome comparedto DCGA as shown in Figure 2(b).

Adckdess;OxOO445eeOA;00445EEO 025FOE37 1D4137SC010445EE8 11620FSC OE6D0861I0044SEFO 2D66 n00000:70044SEB 00 0 0 0j00445F0 09733D62 1E5AOD2

1 00445F 10770C09 272SID33V 00445F1 1212C27 00000000

0,04 45F16 0100Q(100 0000000J" 0044SF20 C0{00445F28 10770C45 lF7FOC6D

'j 00445F30 2D662761 00000000:1}{0044SF38 00000000 00000000-0044SF40 OAS82B4F 1D41OC29

.,:0044SF48 166A031C lF7FOC6D[.l00*45FS0 27392D3E 00000000Fig. 2(a Memory Usagenwithn GA_.

Fig. 2(a) Memory UJsage with GA

Add.e. 15x445ee0Jf00445EE0 3F371F7E 07380B26 ---00445EE 00000000 0000000-

t004415F0 3F FE 20 002,00445F 00000000 0000000'00445F 00000000 00000000t00445F10 0000000 000000:0044SF20 2

010445F38 00000000 00000000,00445F40 OC311CSE 2729006100445F48 00000000 00000000100445FSO 00000000 00000000

:,'. nn jrco onnnfnnn nnnnonnn......

Fig. 2(b) Memory Usage with DCGA

Convergence rate is used as the main criteria to compare theefficiency of GA and DCGA in obtaining a solution foradaptive beam forming. Comparison of convergence ratecomparison with different optimisation technique is shownin Figure 3. 10 simulations were run and the best result ofevery optimisation was obtained. The best fitness functionvalue for the simulation scenario was calculated manuallyand used as benchmark, which is indicated as "ideal" for thesimulation. It can be found that DCGA converged fastercompared to GA as it converged to ideal fitness functionvalue at the range of 2000 generations. However, GA canonly converge to the ideal value at the range of 5000generations.

10

o1

IL

0U0.

0.0110 100 1000

Generation1o0000

Fig.3 Convergence Comparison OfBeam Fonning AssistedBy Different Techniques

Figure 4 shows the fitness function value obtained fromconventional GA and DCGA after 50 independentsimulations with maximum number of generations set to5000 generations. The fitness function values are sorted inascending. It can be seen that most of the fitness functionvalue with GA are more than 0.04; however most of thefitness function value with DCGA are less than 0.04., Thisshows that the convergence rate of DCGA is faster thanconventional GA.

GA0.120 - -

,ia 0.100'0.080

c 0.060-L 0.040U,. 0.020- *y

0.0000 10 20 30 40 50 60

No. of SimulationFig. 4(a) Sorted Fitness Value for GA For 50 Simulations

DOCGA0.120 -

> 0100, 0.080c0.060L 0.040 -

00.020 - _"0.000 -

0 10 20 30 40 50 60No. of Simuation

Fig. 4(b) Sorted Fitness Value for DCGA For 50Simulations

222

7IT 7.7. I

j 7ri.

Simulation result of different crossover selection methodwith different number of best chromosomes kept in pool isshown in Figure 5. Simulation result shows that MatchGuided Selection (MGS) method gives the best result byproducing lowest function value for all cases as compared toconventional selection method such as Roulette Wheel andBest Fit selection.

Fitrness Function Valueo 04000

0.03500 ~ -

0.01500No. of Best

0o010W chromosome1 2 3 4 5 6 7 8 9 10

-& MGS -a Roulette Wheel - Best Fit Selection

Fig. 5 Comparison of Fitness Function Value underDifferent Crossover Selection Method

Figure 6 shows the convergence performance by usingconstant number of crossover points (CCP) and incrementaldynamic crossover points (IDCP) in crossover process.Simulation result showed that IDCP has performed bettercompared to CCP in both GA and DCGA. IDCP is able toproduce lower fitness value, which correspond to lowerpower usage at the maximum crossover point (MCP) set to15% onwards.

Fig. 6 Performance Comparison between CCP and IDCP

assisted adaptive antenna consumes the least power underdifferent traffic load and cell size conditions.

Fig. 7 Total Power Usage at Node B under Different TrafficLoads Condition.

Fig. 8 Total Power Usage at Node B with Different CellSizes.

V. CONCLUSION

Simulation results have shown that Dynamic CharacterizedGenetic Algorithm (DCGA) performs better as compared toconventional GA in assisting adaptive beam forming. In thispaper, the comparison study was carried out by comparingconvergence rate and fitness value obtained by both DCGAand GA for adaptive antenna beam forming. In most of thecases, DCGA converges faster and produce better fitnessvalue which corresponds to lower power usage of adaptiveantenna at Node B.

Simulation was also carried up for dynamic moving UE withPoisson distributed traffic model. 5000 UEs were simulatedunder various traffic loads condition in different cell size.Figure 7 and 8 shows the total power usage at Node B for allantennas elements. Simulation result showed that DCGA

223

a Switch Beam Antenna MAOAS AdaptiveAntenna-_- DCGAAssisted Adaptve Antenna + GAAssisted Adaptive Antenna

o 04000

* 0.035000

0 ~~~~~~~~~~~~-.--GA-CCPU. 0 02500 o~~~~~~~..GA-IDCP

iL 0.02000 ;> .- cAIc

0.01500

4\\10 'A ' 'A A 'A

MACP Percentage

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