加速食物引导的粒子群算法在协作频谱感知中的应用
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摘要
为找到基于合作频谱感知软判决算法中的优化加权系数,最终来优化提高认知无线网络协作频谱感知的检测概率,针对传统的粒子群优化算法进行研究改进。通过赋予每个微粒以生物体的特性,根据它们能量需求的不同,获取当前粒子最需要的信息,选择向个体或群体最优食物源靠近;同时,引入加速变量,运用在粒子的位置更新中,称这种方法为加速食物引导的粒子群优化算法(accelerated food guided particle swarm optimization,afg PSO)。另外,针对噪声环境的不确定性,推导出了噪声不确定最坏情况下的系统检测概率。仿真结果表明,afg PSO算法具有可行性,并且在不同的噪声环境中都能获得更好的频谱检测概率,从而验证了此方法的优越性。对于粒子群算法中的其他参数,还有待进一步改善。
In order to find the optimal weighting coefficient based on cooperative spectrum sensing soft decision algorithm,and then to improve the detection probability of cooperative spectrum sensing in cognitive radio network,this paper studied and improved the traditional particle swarm optimization algorithm. By giving each particle the properties of the organism,according to the difference of energy demand,this paper obtained the needed information at the moment and selected the optimal information of individual or group to close to. Meanwhile,it introduced the acceleration variable,which was used to update the position equation of the particles. This method was called accelerated food guided particle swarm optimization( afg PSO). In addition,for the uncertainty of noise environment,this paper deduced the system detection probability under the worst case of noise uncertainty. The simulation results show that afg PSO algorithm is feasible,and can get a better spectrum detection probability in different noise environment,which proves the superiority of this method. For the other parameters of particle swarm optimization algorithm,it remains to be further improved.
In order to find the optimal weighting coefficient based on cooperative spectrum sensing soft decision algorithm,and then to improve the detection probability of cooperative spectrum sensing in cognitive radio network,this paper studied and improved the traditional particle swarm optimization algorithm. By giving each particle the properties of the organism,according to the difference of energy demand,this paper obtained the needed information at the moment and selected the optimal information of individual or group to close to. Meanwhile,it introduced the acceleration variable,which was used to update the position equation of the particles. This method was called accelerated food guided particle swarm optimization( afg PSO). In addition,for the uncertainty of noise environment,this paper deduced the system detection probability under the worst case of noise uncertainty. The simulation results show that afg PSO algorithm is feasible,and can get a better spectrum detection probability in different noise environment,which proves the superiority of this method. For the other parameters of particle swarm optimization algorithm,it remains to be further improved.
引文
[1]Zhang Wei,Mallik R K,Letaief K.Cooperative spectrum sensing optimization in cognitive radio networks[C]//Proc of IEEE International Conference on Communications.Piscataway,NJ:IEEE Press,2008:3411-3415.
[2]Wang Haijun,Su Xin,Xu Yi,et al.Optimal cooperative energy spectrum sensing in cognitive radio network[J].Frontiers of Electrical and Electronic Engineering in China,2010,5(4):449-455.
[3]王舒,申滨,黄琼,等.认知无线电最优线性协作宽带频谱感知[J].信号处理,2014,30(3):328-336.
[4]Do N T,An B.A soft-hard combination-based cooperative spectrum sensing scheme for cognitive radio networks[J].Sensors,2015,15(2):4388-4407.
[5]Kennedy J,Eberhart R.Particle swarm optimization[C]//Proc of IEEE International Conference on Neural Networks.1995:1942-1948.
[6]Shami T M,El-Saleh A A,Kareem A M.On the detection performance of cooperative spectrum sensing using particle swarm optimization algorithms[C]//Proc of IEEE International Symposium on Telecommunication Technologies.Piscataway,NJ:IEEE Press,2014:110-114.
[7]El-Saleh A A,Ismail M,Ali M A M.Genetic algorithm-assisted soft fusion-based linear cooperative spectrum sensing[J].IEICE Electronics Express,2011,8(18):1527-1533.
[8]郑仕链,楼才义,杨小牛.基于改进混合蛙跳算法的认知无线电协作频谱感知[J].物理学报,2010,59(5):3611-3617.
[9]秦秋燕.基于动物觅食原理的改进微粒群算法研究[D].太原:太原科技大学,2010.
[10]张文召.基于粒子群算法认知无线电联合频谱检测研究[D].成都:西南交通大学,2015.
[11]Cai Xingjuan,Cui Zhihua.Using hungry particle swarm optimization to direct orbits of chaotic systems[C]//Proc of International Conference on Computational Aspects of Social Networks.Washington DC:IEEE Computer Society,2010:7-10.
[12]Paschos A E,Kapinas V M,Hadjileontiadis L J,et al.Dynamic spectrum sensing through accelerated particle swarm optimization[EB/OL].(2015-09-19).https://arxiv.org/abs/510.03840?Context=cs.
[13]Ratnaweera A,Halgamuge S K,Watson H C.Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J].IEEE Trans on Evolutionary Computation,2004,8(3):240-255.
[14]Bogale T E,Vandendorpe L.Max-Min SNR signal energy based spectrum sensing algorithms for cognitive radio networks with noise variance uncertainty[J].IEEE Trans on Wireless Communications,2013,13(1):80-86.
[2]Wang Haijun,Su Xin,Xu Yi,et al.Optimal cooperative energy spectrum sensing in cognitive radio network[J].Frontiers of Electrical and Electronic Engineering in China,2010,5(4):449-455.
[3]王舒,申滨,黄琼,等.认知无线电最优线性协作宽带频谱感知[J].信号处理,2014,30(3):328-336.
[4]Do N T,An B.A soft-hard combination-based cooperative spectrum sensing scheme for cognitive radio networks[J].Sensors,2015,15(2):4388-4407.
[5]Kennedy J,Eberhart R.Particle swarm optimization[C]//Proc of IEEE International Conference on Neural Networks.1995:1942-1948.
[6]Shami T M,El-Saleh A A,Kareem A M.On the detection performance of cooperative spectrum sensing using particle swarm optimization algorithms[C]//Proc of IEEE International Symposium on Telecommunication Technologies.Piscataway,NJ:IEEE Press,2014:110-114.
[7]El-Saleh A A,Ismail M,Ali M A M.Genetic algorithm-assisted soft fusion-based linear cooperative spectrum sensing[J].IEICE Electronics Express,2011,8(18):1527-1533.
[8]郑仕链,楼才义,杨小牛.基于改进混合蛙跳算法的认知无线电协作频谱感知[J].物理学报,2010,59(5):3611-3617.
[9]秦秋燕.基于动物觅食原理的改进微粒群算法研究[D].太原:太原科技大学,2010.
[10]张文召.基于粒子群算法认知无线电联合频谱检测研究[D].成都:西南交通大学,2015.
[11]Cai Xingjuan,Cui Zhihua.Using hungry particle swarm optimization to direct orbits of chaotic systems[C]//Proc of International Conference on Computational Aspects of Social Networks.Washington DC:IEEE Computer Society,2010:7-10.
[12]Paschos A E,Kapinas V M,Hadjileontiadis L J,et al.Dynamic spectrum sensing through accelerated particle swarm optimization[EB/OL].(2015-09-19).https://arxiv.org/abs/510.03840?Context=cs.
[13]Ratnaweera A,Halgamuge S K,Watson H C.Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J].IEEE Trans on Evolutionary Computation,2004,8(3):240-255.
[14]Bogale T E,Vandendorpe L.Max-Min SNR signal energy based spectrum sensing algorithms for cognitive radio networks with noise variance uncertainty[J].IEEE Trans on Wireless Communications,2013,13(1):80-86.