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基于改进粒子群优化的MIMO-OFDM信号检测

DOI: 10.3969/j.issn.1006-7043.201305042

Keywords: MIMO, OFDM, 信号检测, 改进粒子群优化算法, 杂交算法

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Abstract:

针对MIMO-OFDM系统中, 基于粒子群优化的信号检测算法易于陷入局部极值和收敛精度较低的问题, 提出了一种基于改进粒子群优化的MIMO-OFDM信号检测算法。该算法将粒子群优化算法进行改进, 并与遗传算法的杂交技术和极值扰动机制相结合, 对MIMO-OFDM系统进行信号检测。理论研究和仿真结果表明, 在相同误比特率情况下, 所提算法性能优于基于遗传和粒子群优化的MIMO-OFDM信号检测算法性能, 与理想信道下的最大似然检测算法性能相比, 信噪比仅有1 dB的损失;在较少的迭代次数下, 该算法有效地提高了系统的信号检测性能, 有较强的全局搜索能力, 是一种实用的信号检测方法。

References

[1]  PARVEEN N, VENKATESWARLU D S. Implementation of MIMO-OFDM using adaptive multiuser detection in wireless communication[C]//International Conference on Communications, Devices and Intelligent Systems. Kolkata, India, 2012:381-384.
[2]  CHEN Zhiyong, WANG Wenbo, PENG M. Limited feedback scheme based on zero-forcing precoding for multiuser MIMO-OFDM downlink systems[C]//The 5th Annual ICST Wireless Internet Conference. Singapore, 2010:1-5.
[3]  LIU D N, FITZ M P. Low complexity affine MMSE detector for iterative detection-decoding MIMO OFDM systems[J]. IEEE Transactions on Communications, 2008,56(1):150-158.
[4]  XIE Limei. An efficient MIMO detector algorithm for next generation wireless communication[C]//2011 International Conference on Computer Science and Network Technology. Harbin, China, 2011:1195-1199.
[5]  LEE Heunchul, LEE Byeongsi, LEE Inkyu. Iterative detection and decoding with an improved V-BLAST for MIMO-OFDM systems[J]. IEEE Journal on Selected Areas in Communications, 2006,24(3):504-513.
[6]  WUBBEN D, KAMMEYER K D. Low Complexity Successive Interference Cancellation for Per-Antenna-Coded MIMO-OFDM Schemes by Applying Parallel-SQRD [C]//IEEE 63rd Vehicular Technology Conference. Melbourne, Australia, 2006:2183-2187.
[7]  LIU Y, LI Y, LI D, et al. Super-low-complexity qr decomposition-M detection scheme for MIMO-OFDM systems[J]. IET Communications, 2011,5(9):1303-1307.
[8]  ELLIOTT R C, KRZYMIEN W A. Downlink scheduling via genetic algorithms for multiuser single-carrier and multicarrier MIMO systems with dirty paper coding[J]. IEEE Transactions on Vehicular Technology, 2009,58(7):3247-3262.
[9]  LI Fei, WANG Wei, ZHENG Baoyu. A novel detection scheme with quantum genetic algorithm in MIMO-OFDM systems[C]//International Conference on Intelligent Control and Information Processing. Dalian, China, 2010:439-442.
[10]  WANG J S, LAIN J K. Genetical swarm optimization-based symbol detection for MIMO systems[C]//14th Asia-Pacific Conference on Communications. Tokyo, Japan, 2008:1-4.
[11]  ZHAN Zhihui, ZHANG Jun, LI Yun, et al. Adaptive particle swarm optimization[J]. IEEE Transactions on Cybernetics, 2009, 39(6):1362-1381.
[12]  CERVANTES A, GALVAN I M, ISASI P. A new particle swarm method for nearest neighborhood classification[J]. IEEE Transactions on Cybernetics, 2009,39(5):1082-1091.
[13]  KOU Xiaoli, LIU Sanyang, ZHENG Wei. Double-particle swarm optimization with induction-enhanced evolutionary strategy to solve constrained optimization problems[C]//Third International Conference on Natural Computation. Haikou, China, 2007:527-531.
[14]  SHI Yuhui, EBERHART R C. Fuzzy adaptive particle swarm optimization[C]//Proceedings of the 2001 Congress on Evolutionary Computation. Seoul, Korea, 2001: 101-106.
[15]  JUANG Chiafeng. A hybrid of genetic algorithm and particle swarm optimization for recurrent network design[J]. IEEE Transactions on Cybernetics, 2004,34(2):997-1006.

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