Planning System for Indoor Wireless Network

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Wu et al .: Planning System for Indoor Wireless Network

PLANNING SYSTEM FOR INDOOR WIRELESS NETWORKRong-Hou Wu, Yang-Han Lee and Shih-An ChenDepartment of E lectrical EngineeringTamkang UniversityTamsui, Taipei Hsien, Taiwan 251, Republic of ChinaE-mail: yhlee@ee .tku.edu.tw

ABSTRACTA novel Wireless LAN prediction tool usinggenetic algorithm and neural network has beenproposed in this paper. We establish a sitesurvey tool system to predict the ReceivedSignal Strength Index (RSSI) in indoorenvironment. The system includes six items. (1)The fading function: It corrects the functionalcharacteristics of the RSSI for different kinds ofWireless LA N card in free space. (2 ) Setting theattributes of obstacles in indoor environment:The idea of single attribute of local area isproposed in this paper. If there are sameobstacles in one area, we set the area as oneattribute. (3) Genetic Algorithm: We useReproduction, Crossover and Mutation to obtainthe propagation loss through the differentobstacles (Li). (4) Neural Network: We useNeural Network concept to correct the predictionerror arisen from the multipath effect in indoorenvironment. ( 5 ) The auxiliary judgm ent for thesampling points: The method is helpful to usersin establishing the best sampling points. (6 ) Thecalibration of prediction results: We usecalibration to correct the prediction error arisenfrom Li.1. INTRODUCTIONBecause there are many advantages in wirelesscommunication system, such as roaming, easymaintenance, elimination of wiring around theindoor environment, and the flexibility ofrelocating equipment, it becomes the mostattractive alternative to traditional wire-basedand optical-fiber commun ication system [11-[2].In order to develop a unique and compatibleinterface for users, there have been twoarchitectures defined by the IEEE stand in themedia access control (M AC) protocol, which arenamed Infrastructure and Ad Hoc network,respectively. Of these two, the Infrastructurenetwork is a more popular technique because ofits c onvenience for the connection to the Internet.In designing a wireless system based onInfrastructure system, it is difficult to decide thenumbers of access points (AP) and the placewhere A P should be placed in a building. Toobtain the suitable locations of AP, there have

been the two methods: the ray-tracing technique[3]-[5] and the path loss model [ 11, [6]-[SI. It hasbeen found that no matter what the method isemployed, the parameters of computationequation have to be exactly specified. Toovercome several disadvantages of conventionalmethods, the integrated technology of theprediction system for wireless network isproposed in this paper. This prediction systemconsists of two parts: genetic calculator andneural network calculator. The genetic calculatoris employed to computerize the environmentparameters without experiencing thetrial-and-error process. Moreover, the predictionaccuracy can be increased by the neural networkcalculator which is used to modify the predictionerror resulted from the multipath effect. Inaddition, the communication quality in terms ofreceived signal strength index (RSSI) can beread out real time from this prediction system.Furthermore, during the experimental process, itis not necessary to prepare any specialexperiment equipment except the normalnotebook com puters and wireless LAN cards.2. DESIGN O F TH E NOVEL PREDICTIONSYSTEM2.1 Fading FunctionIf we input AutoC AD file to the prediction tool,considering only the interference of theenvironment and disregarding the functionalcharacteristics of different Wireless LAN cards(Fig. I ) , the prediction tool will yield wrongresult. To avoid this, we use the RSSI-distancefrom a different Wireless LAN card for thecorrection process.

Conti-ibuted PaperOriginal manuscript received October 31, 2000 0098 3063/00510.00 001 IEEE

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74 IEEE Transactions on Consumer Electronics, Vol. 47, No . 1 > E B R U A R Y 2001

m E x , ... E,Y;ex, zx,2 . . cx;+lC . Y , 2 C X , ' " ' p:"'EX;ZX:"' .. c x , * "

-rb , , - c P L ( X ) , 1 obtained from the genetic algorithm. In theb, ( 4 ) calculation process, the following equations arcB, = c x ; P L ( x ) , chosen as the fitness function

( 5 )k=mBi =cis,GA,/,, c-r"PL(X),

ID 30 35 40 45 50 55 60 65 mTX.&

Fig. 1 Characteristic among the differentWireless LAN cards2.2 Genetic AlgorithmGenetic algorithm, as first proposed by JohnHolland [9], has become a very important toolapplied in machine leamtng, multiprocessorschedu ling, and function optimization Unlikemany optimization methods, it cansimultaneously search several paths and itsconvergence rate is still faster than othertechniques. Fig. 2shows the operation procedureby using the concept o f genetic algorithm in theproposed prediction system. At first, thepopulation of N, i.e. N chromosomes will begenerated according to the algorithm. In thisresearch, N=20 has been ch osen. In general, thespecific chromosome can be represented by a20-bit sequence which is composed of 0 and 1,and each sequence represents a particularsolution. After randomly processing thepopulation, the fitness value of eachchromosome will be calculated. According tothis value, the candidate for suitablereproduction will be generated. Following thisprocedure, the random selection of one-to-oneamong all the candidates will continue proceedthe crossover and mutation process in order togenerate the next generation. All of theseprocedures will be repeated, including thecalculation of the fitness value, reproduction,crossover, and mutation, until the predeterminedvalue is presented.In this research, the value ofenvironmental parameter will be represented bychromosome, while the suitable solution is

Fig. 2 The operation procedure of the geneticalgorithmWe will only consider the received signalstrength index (RSSI) in direct path withoutconsid ering the multipath effect. Th e followingequation will be employed to determine theRSSI of each neuron.

( 7 )

wherc S is the received signal strength,pT(,y) s the signal value which only path loss istaken into account, 0, is the number of the ithobstacle, L, is the propagation loss through theith obstacle, and n is the index of a specificobstacle. If the value of total error is smaller, thelarger fitness value would be given. Aftercalculating through a maximum of 4,000 times;the prediction system would list out the mostsuitable environmental parameters by referringto total estimation error in the sampling points.2.3 Neural NetworkA computation system constructed from theneural network demonstrates the capability ofsimulatin g the neural network o f living creature


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Wu et al.: Planning System for Indoor Wireless Network

by using a large amount of the artificial neuron.The propagation path of signal between severalneurons is called connection. There exists aweight factor IVYbelonging to each connectionin order to represent the degree of influencewhich the Lth neuron imposes to the j th one.According to the concept of neural network, thefollowing equations are emp loyed to correct theprediction error:

Where Srrepresents the signal value at neuron tafter correction, lfiu is the weighting factorbetween the neuron t and the neuron U, SO s thesignal value at neuron t before correction, Su isthe largest signal value among SL, and Stv issignal values among the other neurons which arelocated at the surrounding of the neuron t.

Fig. 3 illustrates the neural networktechnique which is employed to modify theprediction error arisen from the multipath effectin indoor environment. From this figure, i t isobvious that it will modify the prediction valueat the neuron t by that at the neuron U.

TX

Fig. 3 Illustration o f the neural netw orktechnique which is employed to modify theprediction e rror arisen from the multipath effect2. 4 Sampling PointsWhen Genetic Algorithm is used to obtain thepropagation loss through different obstacles, wecannot know in advance where the best samplingpoints are. We thus design a program to show upthe sampling points automatically.A. As shown in Fig. 4 , sampling points arelocated within a radius of 30m for the center

is AP .B. In this circumstance it established 10sampling points i n which every 72 degreesha s 2 sampling points.C. The program chooses 2 sampling points foreach 72 degrees

1. Choose one sampling point at every 6degrees to have a total of 12 samplingpoints.

2. The program chooses the first one amongth e 12 points(a ) Among the sampling points, we find thesampling point between which and A Pthere are the most categories. The onewith the most categories will be selectedfirst.(b ) When there are more than one samplingpoints with the same amount ofcategories, it will find the samplingpoints with the largest amount ofobstacles between it and AP .(c ) When there are more than one samplingpoints meeting with the aboverequirements, it find the sampling pointwith the longest distance to AP .(d ) When there are still more than onesampling points that meet with theabove requirements, it will pick any oneup randomly.

3. The program chooses the second one amo ngthe 12 points(a ) There should be no less than 18 degrees(b ) The process way is as the same asbetween any two points.described abov e from 2(a) to 2 (d).

D. For the rest 8 points following the steps in C\

1Fig. 4 The auxiliary judgment for the samplingpoints2.5 Calibration P rocedureTh e calibration of large rangeWhen the error rate of RSSI is between 20% an d30%, and as long as any one of the obstaclesmodified its RSSI loss, others obstacles willhave the sam e attributes, too.

n= I

( 1 0 )

,=I

where B, is the loss of RSSI caused by the il hobstacle after correction. L , is th e loss of RSSI


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16 IEEE Transactions on Consumer Electronics, Vol. 47 , No . 1, FEBRUARY 2001

caused by the ithobstacle. B,, is to correct theprediction error of th e i t t l obstacle for the nt hsampling point. ,L7 is the modified coefficient,O


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(c) Finally, we use the prediction results topredict the optimal locations of access points(AP) in indoor environment and illustrate themethod of site survey tool application. Fig. 8shown the prediction result using one APwhich denote by black dot, where the grayarea, white area and dark gray area arerepresenting RSSI of 63-51, 50-31 and30-0, respectively. I n Fig. 9 the gray area,which begins with a smaller area. isbecoming larger when using two APs. Fig10 shown the best prediction result, wherethe three black dots represent the optimal APpositions.

Fig. 8 The prediction result using one A P

and prediction errors also render more accurateprediction values. Finally we use the predictionresults to predict the optimal locations of accesspoints (AP) in indoor environment and illustratethe method of site survey tool application.ACKNOWL E DGME NTThis work was supported by thc NationalScience Council, Taipei, Taiwan, R. 0 . C. underContract NSC 89-221 5-E-032-001REFERENCES

Fig. 9 The prediction result using two AP s

Fig. 10 The best prediction result o f the optimalAP positions4. CONCL USIONThe advantages of the site survey tool are asfollows: By classifying different obstructers bycolor, the users can obtain a clear view on thelocation distribution of obstructers in the indoorenvironment. The auxiliary judgment for thesampling points is helpful to users inestablishing the best sampling points. Theapplications of Genetic Algorithm and methodof Neural Network result in more preciseprediction values and an increased calculationspeed. The calibration of Wireless LAN cards

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D.C.Cox, Universal portable radiocommunications, IEEE Trans. Veh.Technol. , vol. VT-34, no. 3, pp.117-121,Aug. 1985.M. A. Panjwani, et. al., Interactivecomputation of coverage regions forwireless communication in multiflooredindoor environments, IEEE Journal onSelected Areas in Comm un., vol. 14, no.3 , pp . 420-429, April 1996.J . W. McKown , et. al. , Ray tracing as adesign tool for radio networks, T E ENetwork Mag., vol. 5 . pp.27-31, Nov.1991.W. Honcharenko, et. al. , Mechanismsgoverning propagation between floors inbuildings, IEEE Trans. AntennasPropaga., vol. 41, no. 6, pp.787-790,1993.T. S. Rappaport, et. al., Site-specificpropagation prediction for PCS systemdesign, i n Wireless PersonalCommunicat ions , M . J . Feuerstein andT.S. Rappaport, Eds. Norwell MA:Kluwer, 19 93, pp. 281-315 .K. W. Cheung, et. al., A new empiricalmodel for indoor propagation prediction,IEEE Trans. Veh. Technol., vol. 47, no. 8,C. C. Chiu, et. al., Coverage predictionin indoor wireless communication,IEICE Trans. Co mm un., vol. E79-B, no.9, pp. 13 46-1350, Sep. 1996.H. Lee, ct. AI., Site sury tool fo rwireless network based onauto-c alibrat ion.J . H. Holland, Adaptation i n Natural andArtificial Systems. University ofMichigan Press 1975 . (Second edition:MIT press, 1992)

pp. 996-1001, Aug. 1998.


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Obstacle

IEEE Transactions on Consumer Electronics, Vol. 47, No. 1, FE BRUARY 2001

Table 1. Implementation result of the first experiment

RSSI loss

Table 2 . Implementation result of th e sccond experiment

Wood wall

Table 3 . The RSSI loss ofthe different obstacles

1.5-2.5Cement wall 4-6Lobby wall 6-8


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BIOGRAPHIES

Rong-Hou Wu was born inTaipei, Taiwan, Republic ofChina, on October 12, 1956He received hi s B.S. degrecin Electronic Engineering in1981 from the TamkangUniversity, Tamsui, Taiwan.He received his M.S. degreein Electrical and Computer

Engineering in 1987 from the University ofMassachusetts Dartmouth. He is currently a Ph.D. candidate of the Department of ElectricalEngineering, Tamkang University, Tamsui,Taiwan. His research interests includeFiber-optical communication and computercommu nication networks.

Yang-Han Lee was born inTaipei, Taiwan, Republic ofChina, in 1964. He receivedth e B.S., M.S. , and Ph.Ddegrees in electrical-engineering from NationalTaiwan University, Taipei, in1987, 1989, and 1991,

respectively. From 1992 to 1994, he was on dutyin the Air Force He joined the faculty of theDepartment of Electrical Engineering, T amkan gUniversity, Taipei, as an Associate Professor.Hi s main research interests include optical fibercommunication systems and communicationelectronics.

Shih-An Chen received thcB.S. degree from Feng ChiaUniversity, Taiwan, R.O.C.in 19 98 and the M.S. degreefrom Tamkang University,Taiwan, in 2000, all inelectrical engineering. Hisresearch interests includewireless communication,optical communication, and communicationtheory.

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Planning System for Indoor Wireless Network