International Power, Electronics and Materials Engineering Conference (IPEMEC 2015)

Size: px

Start display at page:

Download "International Power, Electronics and Materials Engineering Conference (IPEMEC 2015)"

Rosanna Powell
5 years ago
Views:

1 Iteratioal Power, Electroics ad Materials Egieerig Coferece (IPEMEC 205) etwork Mode based o Multi-commuicatio Mechaism Fa Yibi, Liu Zhifeg, Zhag Sheg, Li Yig Departmet of Military Fiace, Military Ecoomy Academy, Wuha City Hubei Provice ZipCode , Chia fayibi_wh@63.com Keywords: Cogitive Radio, Eergy-savig, PSO (Particle Swarm Optimizatio), Aealig algorithm Abstract. For the sake of improvig gree eergy-savig efficiecy of the radio commuicatio system,we proposed a gree eergy-savig algorithm for multichael commuicatio system based o the eergy cosumptio model aalysis. Firstly, mathematical model of the multi-chael eergy cosumptio miimizatio problem was built. Secodly, we used PSO algorithm to solve mathematical model, ad aealig operatio of simulated aealig algorithm was itroduced ito the PSO algorithm, which would prevet the local optimal solutio ad promote the speed of solvig. I the ed, we used the simulatio cotrast experimets to test our algorithm performace. The results show that our algorithm ca meet differet user service requiremets, ad ca reduce total trasmissio power ad eergy cosumptio of the system, which is i favor of eviromet protectio. Itroductio Alog with the popularizatio ad mature of computer ad movig commuicatio techology, mobile user data icrease sharply, which brigs soarig eergy cosumptio ad eviromet problems. Eergy cosumptio has attracted extesive attetio, ad the eergy related ecoomic cost has reduced the telecom profits. Therefore, we proposed the gree commuicatio techology to save eergy. Gree commuicatio techology ot oly reduce emissio of CO 2 ad disadvatageous effect o eviromet, but also reduce telecom ecoomic cost, which has become the curret research highlights all over the world [][2]. For the sake of system power reductio ad improvig eergy utilizatio, we proposed a ew gree eergy-savig algorithm for multichael commuicatio system. Firstly, we built a mathematical model for multichael power cosumptio miimizatio problem. Secodly, we used combiatioal algorithm of SA(simulated aealig algorithm) ad PSO (particle swarm optimizatio algorithm)to solve this model. I the ed, we used the simulatio cotrast experimets to test our algorithm performace. 2 Power Cosumptio Miimizatio Problem of Multichael 2. System Model Assumig that all chaels are free ad ca be used by cogitive radio system, the power cosumptio miimizatio problem of multichael ca be described as followig: mi Pˆ { P } = st.. b = = b Where, is the umber of chaels, b is target speed, p is trasmissio power of brach, Pˆ is system eergy cosumptio, adb is real speed of brach. () 205. The authors - Published by Atlatis Press 557

2 2.2 Eergy cosumptio model The system eergy cosumptio of brach also ca be modeled as followig: ˆ P P = η (2) Where, η is the average efficiecy of PA i brach. The approximate model of average PA efficiecy is : η = η( P) (3) Therefore, the eergy cosumptio expressio of chael power amplifyig system is: a ˆ P.max a P = P (4) ηmax. Iformed by expressio (4), the power cosumptio of the system depeds o active chael umber. Ad we ca covert the problems ito trasmissio power miimum value problem, uder the coditio of satisfyig target certai rate limit, ad uder the coditio of system power miimum cosumptio [2].Supposig i the multi-chael ad SR(sigal to oise ratio) kow coditios, we choose chaels of these as active chaels to trasmit power, ad at this momet, total power cosumptio of the system is as followig: ˆ P.max P = (5) η max. A Where, is the umber of active chaels. As a result, we list the Lagrage equatio about PA miimum trasmissio power: f = P + l b b = = = P + l b l + Pg ( ) = l 2 = I equatio (6), we get P ad λ partial derivatives as followig: f l = 0 = P l 2 g + P f = l ( + Pg ) b = 0 (8) l l 2 = For the equatio (), we used combiatioal algorithm of SA (simulated aealig algorithm) ad PSO (particle swarm optimizatio algorithm) to solve it. 3 Simulated Aealig Particle Swarm Optimizatio Algorithm(SA-PSO) 3. particle swarm algorithm I the PSO, each iitialized particle represets the solutio of optimizatio problem, ad each particle ca be evaluated its quality by fitess fuctio i each iteratio process. Ad i the searchig process, each idividual particle s optimal solutio ad whole populatio s global optimal solutio are updated costatly. I the ext iteratio, each particle s speed ad positio are updated by tracig two extreme values i the last iteratio. Accordigly, we get the global optimal solutio by costat iteratio. The equatio of speed ad positio are as followig: k+ k k k k k Vid = ωvid + cr( Pid Xid ) + cr 22( Pgd Xid ) (9) k+ k k+ Xid = Xid + Vid Where, d is the dimesios of solutio space, i is the particle umber i the populatio, ωis the iertia weight, k is curret iteratio umber, c,c2 are the acceleratio factor, r, r2 are the radom umber i [0,] iterval. (6) (7) 558

3 3.2 simulated aealig algorithm simulated aealig'algorithm is a kid of heuristic algorithm with high partial search ability, whose simulated solid aealig process is suitable for solvig large scale combiatorial optimizatio problem, ad uses Metropolis criteria to accept probability P of the optimal solutio, as followig: f( i) f( i') P = f( i) f( i') (0) exp( ) f( i) > f( i') t Where, f(i) is the objective fuctio of the problems, t is cotrols parameter [4]. Supposig that T(t) represets temperature at t momet, the SA simulates coolig way as followig: T0 Tt () = () lg( + t) Sphere fuctio has oly oe global optimal value; Griewak fuctio is ot smooth ad cotiuous i the ear global optimal value; Rastrigi is pathological fuctio hardly fidig the global optimal value; the ruig results of PSO ad SA-PSO is as show i figure 2. Iformed by figure, the covergece rate of SA-PSO is much better tha comparig algorithm i all fuctios, which idicates SA-PSO has much better global search ability, covergece accuracy ad rate. (a) Sphere fuctio fitess covergece curve (b) Griewak fuctio fitess evolutio curve 559

4 (c) Rastrigi fuctio fitess evolutio curve Figure performace compariso betwee SA-PSO ad PSO 4 Simulatio Eviromet I order to test the gree eergy-savig performace of SA-PSO, we choose Matlab 202 to make simulatio experimet uder the coditio of Widows XP with P4 double cores CPU ad 4GRAM. At the same time, we carry o compariso experimet betwee SA ad PSO to make this article more covicig. 4. Eergy utilizatio compariso For 3 chaels commuicatio system, eergy utilizatio of several algorithm is as show i figure 2. Iformed by figure 2, compared with the sigle SA or PSO, the average eergy utilizatio of SA-PSO is the highest, ad the eergy-savig efficiecy is greatly improved up to75.02%, which is far more tha 6.33% of SA or 66.08% of PSO. This maily because SA-PSO itegrates GA (geetic algorithm) strog global search ability ad SA strog local search ability. This algorithm ca overcome the local optimal solutio ad choose maximum SR chael to use, ad ca miimize trasmissio power, which could achieve the eergy-savig effect. 80 SA 75 PSO 70 SA-PSO chael chael2 chael3 chael umber Figure 2 the eergy utilizatio cotrast by differet algorithms eergy utilizatio /% 4.2 performace aalysis of differet chaels I order to further test SA-PSO uiversality, we take three differet user service requiremets ito accout, as show i table. Patter is used i video commuicatio, ad patter 2 is used i voice commuicatio, ad patter 3 is used i LDR (low data rate). Table descriptio of differet patters patter patter 2 patter 3 BER tar DER tar 300kbps 0kbps 200kbps 560

5 The maximum bit error rate, data rate ad total trasmissio power of differet algorithms for differet patters are show i figure2. Iformed by figure 2, i the coditio of meetig differet customers service request, total trasmissio power ad maximum bit error rate of SA-PSO is miimum, ad the data rate is maximum. The results idicate that SA-PSO ca efficietly reduce eergy cosumptio, ad is more i favor of eviromet protectio. 5 Coclusios Gree, cogitive radio is a kid of gree commuicatio techology. I order to improve eergy utilizatio ad reduce eergy cosumptio, we propose a gree eergy-savig algorithm based o the eergy cosumptio model aalysis, ad make performace aalysis by simulatio experimet. Simulatio experimet idicates that compared with other algorithms, our algorithm ot oly reduce total trasmissio power of the system, but also better meet customer service requiremets for quality. Refereces []Lv, Zhiha, Liagbig Feg, Haibo Li, ad Shegzhog Feg. "Had-free motio iteractio o Google Glass." I SIGGRAPH Asia 204 Mobile Graphics ad Iteractive Applicatios, p. 2. ACM, 204. [2]Li, Wubi, Joha Tordsso, ad Erik Elmroth. "A aspect-orieted approach to cosistecy-preservig cachig ad compressio of web service respose messages." I Web Services (ICWS), 200 IEEE Iteratioal Coferece o, pp IEEE, 200. [3]Lv, Zhiha, ad Tiayu Su. "3D seabed modelig ad visualizatio o ubiquitous cotext." I SIGGRAPH Asia 204 Posters, p. 33. ACM, 204. [4]Lv, Zhiha, Liagbig Feg, Shegzhog Feg, ad Haibo Li. "Extedig Touch-less Iteractio o Visio Based Wearable Device." Virtual Reality (VR), 205 ieee. IEEE, 205. [5]Zhag, Megxi, Zhiha Lv, Xiaolei Zhag, Ge Che, ad Ke Zhag. "Research ad Applicatio of the 3D Virtual Commuity Based o WEBVR ad RIA." Computer ad Iformatio Sciece 2, o. (2009): p84. 56

Application of Improved Genetic Algorithm to Two-side Assembly Line Balancing

Application of Improved Genetic Algorithm to Two-side Assembly Line Balancing 206 3 rd Iteratioal Coferece o Mechaical, Idustrial, ad Maufacturig Egieerig (MIME 206) ISBN: 978--60595-33-7 Applicatio of Improved Geetic Algorithm to Two-side Assembly Lie Balacig Ximi Zhag, Qia Wag,