MMSE准则下近似最优MIMO分组并行检测算法

doi:10.3969/j.issn.0372-2112.2013.10.002

电子学报

MMSE准则下近似最优MIMO分组并行检测算法

芮国胜¹, 张海波¹, 田文飚¹, 张洋¹, 邓兵², 李廷军²

1. 海军航空工程学院信号与信息处理山东省重点实验室, 山东烟台 264001;
2. 海军航空工程学院电子信息工程系, 山东烟台 264001

收稿日期:2012-09-27 修回日期:2012-12-24 出版日期:2013-10-25
- 作者简介:
- 芮国胜 男.1968年3月生,山东省烟台市人.2001年获哈尔滨工业大学信号与信息处理专业博士学位,现为海军航空工程学院电子信息工程系教授.研究方向为通信信号处理、小波理论及其应用等. E-mail:ruigs@sina.com张海波男.1983年5月出生,河北省保定市人.2009年毕业于海军航空工程学院,并获硕士学位,现为该校信息与通信工程专业博士研究生.研究方向集中于MIMO技术,格归约理论及其应用等. E-mail:zhbemail@126.com
- 基金资助:
- "泰山学者"建设工程专项经费资助; 国家自然科学基金 (No.60902054); 中国博士后科学基金 (No.20090460114,No.201003758)

A Near-Optimal Parallel Detection Algorithm Based on Channel Partition and MMSE Criterion

RUI Guo-sheng¹, ZHANG Hai-bo¹, TIAN Wen-biao¹, ZHANG Yang¹, DENG Bing², LI Ting-jun²

1. Signal and Information Processing Provincial Key Laboratory in Shandong, Naval Aeronautical and Astronautical University, Yantai, Shandong 264001, China;
2. Department Electronic Engineering, Naval Aeronautical and Astronautical University, Yantai, Shandong 264001, China

Received:2012-09-27 Revised:2012-12-24 Online:2013-10-25 Published:2013-10-25
- Supported by:
- supported by Taishan Scholars Special Funding for Construction Projects; National Natural Science Foundation of China (No.60902054); China Postdoctoral Science Foundation (No.20090460114, No.201003758)

摘要/Abstract

摘要： 在采用多天线高阶QAM的MIMO通信系统中,现有基于信道分组并行检测算法虽然接近最优检测性能但以牺牲计算效率为代价.针对这一问题,本文提出一种MMSE准则下基于信道分组的并行检测算法,不但有效降低计算复杂度,而且仍保证检测性能.该算法采用MMSE准则下格归约算法改进分组后条件较好子信道矩阵特性,并在消除参考信号基础上利用改进的子信道矩阵对剩余信号以非线性方式进行检测.仿真结果表明:对4×4和6×6MIMO系统,该算法检测性能达到最优,对于8×8 MIMO系统,比最优算法所需信噪比提高约1dB.复杂度分析表明:相比现有信道分组检测算法,相同检测性能下该算法在6×6 MIMO系统中复杂度降低90%以上,在8×8 MIMO系统中复杂度降低98%以上.

关键词: 多输入多输出系统, 最小均方误差, 信道分组, 并行检测, 格归约, 正交幅度调制(QAM)

Abstract: For the MIMO systems with a large number of antennas and a large QAM constellation,the existing parallel detection algorithms,which are based on channel partition,can approach the optimal detection performance but the computational efficiency is sacrificed.In order to solve the problem,a new parallel detection algorithm based on channel partition is proposed.This algorithm can not only efficiently reduce the computational complexity but also guarantee the detection performance.After channel partition,this algorithm firstly employs the lattice reduction algorithm under the MMSE criterion to improve the properties of the sub-channels;and then the remaining signals are detected with a nonlinear method.The simulation results show that the proposed algorithm achieves the near-optimal detection performance for the 4*4 and 6*6 MIMO systems and its performance decreases 1dB for the 8*8 MIMO systems compared to the optimal performance.Complexity analysis shows that its complexity is reduced 90% or above for 6*6 MIMO systems and 98% or above for 8*8 MIMO systems at the same BER.

Key words: multiple-input multiple-output(MIMO), minimum mean square error(MMSE), channel partition, parallel detection, lattice reduction, QAM

中图分类号:

TN929

芮国胜, 张海波, 田文飚, 等. MMSE准则下近似最优MIMO分组并行检测算法[J]. 电子学报, DOI: 10.3969/j.issn.0372-2112.2013.10.002.

RUI Guo-sheng, ZHANG Hai-bo, TIAN Wen-biao, et al. A Near-Optimal Parallel Detection Algorithm Based on Channel Partition and MMSE Criterion[J]. Acta Electronica Sinica, DOI: 10.3969/j.issn.0372-2112.2013.10.002.

参考文献

[1] 张继良,等.6.0-6.4GHz室内MIMO无线信道测量与传播特性分析[J].电子学报,2012,40(6):1213-1217. J Zhang,et al.Indoor measurement and characterization of wireless MIMO channel at 6.0-6.4GHz[J].Acta Electronica Sinica,2012,40(6):1213-1217.(in Chinese)
[2] 傅海阳,陈技江,曹士坷.MIMO系统和无线信道容量研究[J].电子学报,2011,39(10):2221-2229. H Fu,J Chen,S Cao.Study on the MIMO system and wireless channel capacity[J].Acta Electronica Sinica,2011,39(10):2221-2229.(in Chinese)
[3] 张明,张建华,高新颖.一种通用宽带MIMO信道模型[J].电子学报,2006,34(10):1758-1762. M Zhang,J Zhang,X Gao.A generalized broadband MIMO channel model[J].Acta Electronica Sinica,2006,34(10):1758-1762.(in Chinese)
[4] S Aubert,M Mohaisen.From linear equalization to lattice-reduction-aided sphere-detector as an answer to the MIMO detection problematic in spatial multiplexing systems[J].Vehicular Technologies:Increasing Connectivity,2011,4(4):71-96.
[5] J G Proakis,M Salehi.Digital Communications,5th Edition[M].New York:McGraw-Hill,2007.
[6] 易新平,唐友喜,邵士海.分布式发射天线MIMO信号的最优线性检测[J].电子学报,2009,37(12):2694-2699. X Yi,Y Tang,S Shao.Optimal linear detection algorithm of MIMO with distributed transmit antennas[J].Acta Electronica Sinica,2009,37(12):2694-2699.(in Chinese)
[7] Y S Cho,J Kim,W Y Yang.MIMO-OFDM Wireless Communications with MATLAB[M].Singapore:John Wiley & Sons(Asia),2010.
[8] C Won-Joon,R Negi,J M Cioffi.Combined ML and DFE decoding for the V-BLAST system.Proceedings of IEEE International Conference on Communications [C].New Orleans,LA:IEEE,2000.1243-1248.
[9] 方娟.MIMO系统并行检测算法研究 [D].西安:西安电子科技大学,2011. J Fang.Research on Parallel Detection Algorithm for MIMO Systems [D].Xi'an:Xidian University,2011.(in Chinese)
[10] 熊聪.V-BLAST系统近似最优检测算法研究 [D].北京邮电大学,2010. X Xiong.Research on Algorithm for Near-optimal Detection For V-BLAST Systems [D].Beijing:Beijing University of Posts and Telecommunications,2010.(in Chinese)
[11] Z Luo,M Zhao,S Liu.Generalized parallel interference cancellation with near-optimal detection performance[J].IEEE Transactions on Signal Processing,2008,56(1):304-312.
[12] C Xiong,et al.An efficient parallel algorithm with partial decision feedback for near-optimal MIMO detection [A].Proceedings of IEEE Global Telecommunications Conference,(GLOBECOM).Hawaii,USA:IEEE,2009.1-5.
[13] D W Waters,J R Barry.Noise-predictive decision-feedback detection for multiple-input multiple-output channels[J].IEEE Transactions on Signal Processing,2005,53(5):1852-1859.
[14] D Wübben,D Seethaler,J Jaldén.Lattice reduction[J].IEEE Signal Processing Magazine,2011,28(3):70-91.
[15] G Hung,C Ling,M Ho.Complex lattice reduction algorithm for low-complexity full-diversity MIMO detection[J].IEEE Transactions on Signal Processing,2009,57(7):2701-2710.
[16] D Wubben,R Bohnke,V Kuhn.MMSE-based lattice-reduction for near-ml detection of MIMO systems [A].Proceedings of ITG Workshop on Smart Antennas [C].Germany:IEEE,2004.106-113.
[17] B J Gestner.Lattice Reduction for MIMO Detection:From Theoretical Analysis to Hardware Realization [D].Georgia Institute of Technology,2011.
[18] J Jalden,D Seethaler,G Matz.Worst- and average-case complexity of LLL lattice reduction in MIMO wireless systems [A].Proceedings of ICASSP [C].Las Vegas:IEEE,2008.2685-2688.

MMSE准则下近似最优MIMO分组并行检测算法

A Near-Optimal Parallel Detection Algorithm Based on Channel Partition and MMSE Criterion

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	黄河. 用于大规模多输入多输出系统的波束收缩偶极子天线[J]. 电子学报, 2022, 50(9): 2299-2304.
[2]	刘凯, 陈盼盼. 最佳及几乎最佳高斯整数ZCZ序列集的构造[J]. 电子学报, 2018, 46(3): 755-760.
[3]	曹杰, 廖勇, 王丹, 周昕, 李瑜锋. 基于非理想CSI的下行MU-MIMO鲁棒波束成形[J]. 电子学报, 2016, 44(9): 2093-2099.
[4]	顾晨阳, 杨瑞, 盛文钦, 李丁山. 单载波频域均衡系统中一种简化的IBDFE算法[J]. 电子学报, 2014, 42(9): 1699-1704.
[5]	徐立中, 冯肖亮, 文成林. 网络化多传感器系统的序贯式融合滤波——基于噪声估计方法[J]. 电子学报, 2014, 42(1): 160-168.
[6]	吴锡, 周激流, 谢明元. 改进LMMSE的弥散加权磁共振图像Rician噪声复原[J]. 电子学报, 2013, 41(4): 717-721.
[7]	葛泉波;冯肖亮;徐廷梁. 不确定无序量测系统的最优网络化状态估计[J]. 电子学报, 2011, 39(3A): 7-13.
[8]	易新平;唐友喜;邵士海;吴桐. 分布式发射天线MIMO信号的最优线性检测[J]. 电子学报, 2009, 37(12): 2694-2699.
[9]	程履帮. OFDMA系统中基于LMMSE信道估计算法的改进及其性能分析[J]. 电子学报, 2008, 36(9): 1782-1785.
[10]	许威;赵春明;史志华;周鹏. MIMO系统中基于ZF/MMSE检测的自适应功率分配方案[J]. 电子学报, 2008, 36(10): 1891-1896.
[11]	赵晋;张建秋;高羽. 迭代异方差估计及其在多传感器数据融合中的应用[J]. 电子学报, 2008, 36(10): 1938-1943.
[12]	曹琲琲, 李建东, 杨家玮. 频率选择性快衰落信道中的MIMO-OFDM系统检测算法研究[J]. 电子学报, 2007, 35(6A): 46-53.
[13]	李潇, 金石, 高西奇. 发送端已知信道均值或协方差信息的MIMO系统的功率分配[J]. 电子学报, 2007, 35(6A): 169-173.
[14]	姜永权, 周渊平, 柳平, 马逸新. 窄带波束信道MIMO系统的容量分析[J]. 电子学报, 2006, 34(3): 445-449.
[15]	李琳;周文辉;谭述森. 扩频系统中最小误码率意义下的最优干扰抑制技术[J]. 电子学报, 2005, 33(1): 32-37.