基于混合鲸鱼优化算法的鲁棒多用户检测
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摘要
针对冲击噪声环境下多用户检测误码率高的问题,提出一种基于混合鲸鱼优化的鲁棒多用户检测算法。该算法首先利用基于非线性控制策略的改进鲸鱼优化算法,加速寻优算法迭代过程的收敛;再利用自适应差分进化算法丰富算法种群个体信息,增强优化算法的全局收敛性;同时将适应度较好的个体信息保存到集合中,以保证下一次迭代寻优方向的可靠性,最终实现对最优解位置的快速解算。仿真结果表明,基于本文算法设计的多用户检测器相比采用遗传算法、差分进化算法,以及鲸鱼优化算法的多用户检测器寻优迭代次数更少,且误码率低。
A robust multiuser detection algorithm based on hybrid whale optimization is proposed to solve the problem of high bit error rate of multiuser detection under impulse noise environment. Firstly,the improved whale optimization algorithm based on the non-linear control strategy is used to accelerate the convergence of the iteration process of the optimization algorithm. Then,the adaptive differential evolution algorithm is used to enrich the individual information of the population and enhance the global convergence of the optimization algorithm. At the same time,the individual information with good fitness is saved in a set to ensure the reliability of the optimization direction of the next iteration,and finally,a fast resolution to the position of the optimal solution is achieved. The simulation results show that the multi-user detector based on the proposed algorithm has fewer iterations and lower bit error rate than the multi-user detector based on genetic algorithm,differential evolution algorithm and whale optimization algorithm.
A robust multiuser detection algorithm based on hybrid whale optimization is proposed to solve the problem of high bit error rate of multiuser detection under impulse noise environment. Firstly,the improved whale optimization algorithm based on the non-linear control strategy is used to accelerate the convergence of the iteration process of the optimization algorithm. Then,the adaptive differential evolution algorithm is used to enrich the individual information of the population and enhance the global convergence of the optimization algorithm. At the same time,the individual information with good fitness is saved in a set to ensure the reliability of the optimization direction of the next iteration,and finally,a fast resolution to the position of the optimal solution is achieved. The simulation results show that the multi-user detector based on the proposed algorithm has fewer iterations and lower bit error rate than the multi-user detector based on genetic algorithm,differential evolution algorithm and whale optimization algorithm.
引文
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11 Oliva D,Aziz M A E,Hassanien A E. Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm[J]. Applied Energy,2017; 200:141-154
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13 高洪元,梁炎松,刘丹丹.基于新量子蜂群算法的鲁棒多用户检测[J].计算机工程,2016; 42(9):48-51Gao Hongyuan,Liang Yansong,Liu Dandan. Robust multiuser detection based on novel quantum bee colony algorithm[J]. Computer Engineering,2016; 42(9):48-51
14 闫学青,高兴宝.基于种群分类的差分进化算法[J].纺织高校基础科学学报,2017; 30(2):272-278Yan Xueqing,Gao Xingbao. Differential evolution algorithm based on population classification[J]. Basic Sciences Journal of Textile Universities,2017; 30(2):272-278
15 Zhang J,Sanderson A C. JADE:Adaptive differential evolution with optional external archive[J]. IEEE Transactions on Evolutionary Computation,2009; 13(5):945-958
16 Rahnamayan S,Tizhoosh H R,Salama M M A. Opposition-based differential evolution[J]. IEEE Transactions on Evolutionary Computation,2008; 12(1):64-79
17 Yen K,Hanzo L. Genetic algorithm assisted multiuser detection in asynchronous CDMA communications[C]//IEEE International Conference on Communications. New York:IEEE,2001:826-830
18 Ababneh J I,Aldalgamouni T F,Alqudah A A. Minimum bit error rate multiuser detection of SDMA-OFDM system using differential evolutionary algorithm[C]//IEEE International Conference on Wireless and Mobile Computing,Networking and Communications.New York:IEEE,2010:273-279
2 Zhou H,Woo W L,Sharif B S. Subspace-based blind adaptive multiuser detection using kalman filter[J]. IEEE Communication Proceedings,2005:152(3):302-310
3 Kazemitabar J,Jafarkhani H. Performance analysis of multiple antenna multi-user detection[C]//Information Theory and Applications Workshop. New York:IEEE,2009:150-159
4 Ergun C,Hacioglu K. Multiuser detection using a genetic algorithm in CDMA communications systems[J]. IEEE Transactions on Communications,2000; 48(8):1374-1383
5 Hung H L,Lee S H,Wen J H. PSO algorithm for multiuser detector in asynchronous CDMA communication systems[C]//2006 IEEE Region 10 Conference. New York:IEEE,2006:1-4
6 Xia J,Lv T,Yun X,et al. Simulated annealing based multiuser detection for synchronous SDMA system[C]//IEEE Singapore International Conference on Communication Systems. New York:IEEE,2008:441-445
7 Mirjalili S,Lewis A. The whale optimization algorithm[J]. Advances in Engineering Software,2016; 95:51-67
8 Aljarah I,Faris H,Mirjalili S. Optimizing connection weights in neural networks using the whale optimization algorithm[J]. Soft Computing,2016; 22(1):1-15
9 Aziz M A E,Ewees A A,Hassanien A E. Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation[J]. Expert Systems with Applications,2017; 83:242-256
10 Dao T K,Pan T S,Pan J S. A multi-objective optimal mobile robot path planning based on whale optimization algorithm[C]//IEEE International Conference on Signal Processing. New York:IEEE,2017:337-342
11 Oliva D,Aziz M A E,Hassanien A E. Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm[J]. Applied Energy,2017; 200:141-154
12 Shengdong X,Mingsheng Z,Sainan S. Research on the performance of the time delay estimation in the multi-path wireless channel based on super-resolution algorithms[J]. Journal of Convergence Information Technology,2012; 7(17):416-423
13 高洪元,梁炎松,刘丹丹.基于新量子蜂群算法的鲁棒多用户检测[J].计算机工程,2016; 42(9):48-51Gao Hongyuan,Liang Yansong,Liu Dandan. Robust multiuser detection based on novel quantum bee colony algorithm[J]. Computer Engineering,2016; 42(9):48-51
14 闫学青,高兴宝.基于种群分类的差分进化算法[J].纺织高校基础科学学报,2017; 30(2):272-278Yan Xueqing,Gao Xingbao. Differential evolution algorithm based on population classification[J]. Basic Sciences Journal of Textile Universities,2017; 30(2):272-278
15 Zhang J,Sanderson A C. JADE:Adaptive differential evolution with optional external archive[J]. IEEE Transactions on Evolutionary Computation,2009; 13(5):945-958
16 Rahnamayan S,Tizhoosh H R,Salama M M A. Opposition-based differential evolution[J]. IEEE Transactions on Evolutionary Computation,2008; 12(1):64-79
17 Yen K,Hanzo L. Genetic algorithm assisted multiuser detection in asynchronous CDMA communications[C]//IEEE International Conference on Communications. New York:IEEE,2001:826-830
18 Ababneh J I,Aldalgamouni T F,Alqudah A A. Minimum bit error rate multiuser detection of SDMA-OFDM system using differential evolutionary algorithm[C]//IEEE International Conference on Wireless and Mobile Computing,Networking and Communications.New York:IEEE,2010:273-279