基于Kaczmarz迭代的大规模MIMO系统低复杂度软输出信号检测

doi:10.3969/j.issn.0372-2112.2018.11.023

电子学报 ›› 2018, Vol. 46 ›› Issue (11): 2746-2752.DOI: 10.3969/j.issn.0372-2112.2018.11.023

基于Kaczmarz迭代的大规模MIMO系统低复杂度软输出信号检测

申滨¹, 赵书锋¹, 黄龙杨²

1. 重庆邮电大学, 重庆 400065;
2. 中国民用航空飞行学院, 四川德阳 618300

收稿日期:2017-12-26 修回日期:2018-06-04 出版日期:2018-11-25
- 作者简介:
- 申滨男,1978年出生,博士,重庆邮电大学教授,主要研究方向为LTE、5G系统和认知无线电等.E-mail:shenbin@cqupt.edu.cn;赵书锋 男,1991年出生,重庆邮电大学硕士生,主要研究方向为LTE系统、大规模MIMO系统等.E-mail:zhaoshuf@foxmail.com
- 基金资助:
- 国家科技重大专项基金 (No.2016ZX03001010-004）

Low-Complexity Soft-Output Signal Detection Based on Kaczmarz Method for Uplink Massive MIMO Systems

SHEN Bin¹, ZHAO Shu-feng¹, HUANG Long-yang²

1.Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2.Civil Aviation Flight University of China, Deyang, Sichuan 618300, China

Received:2017-12-26 Revised:2018-06-04 Online:2018-11-25 Published:2018-11-25
- Supported by:
- National Science and Technology Major Project of the Ministry of Science and Technology (No.2016ZX03001010-004)

摘要/Abstract

摘要： 大规模MIMO系统上行链路中，最小均方误差（MMSE）算法能获得接近最优的线性检测性能，但是涉及复杂度较高的矩阵求逆运算.本文基于Kaczmarz迭代提出一种低复杂度软输出信号检测算法，在算法实现中避免了矩阵求逆运算，将实现复杂度由O（K³）降为O（K²）.同时，引入了最优松弛参数进一步加快算法收敛，最后给出了两种用于信道译码的LLR的近似计算方法.仿真结果表明：所提出的Kaczmarz迭代软输出信号检测算法经过两到三次简单的迭代即可较快地收敛，并达到接近MMSE检测算法的误码率性能的水平，其性能与复杂度均优于基于矩阵近似求逆的一类检测算法.

关键词: 大规模MIMO, 低复杂度, Kaczmarz迭代, 松弛参数, 软输出

Abstract: For massive MIMO system uplink, conventional minimum mean square error (MMSE) linear detection algorithm can achieve nearly optimal performance, but it involves complicated matrix inversion. A low-complexity detection algorithm based on Kaczmarz method is proposed in this paper, which can circumvent the matrix inverse operation and hence reduce the complexity from O (K³) to O (K²). Meanwhile, an optimal relaxation parameter is introduced to further accelerate the algorithm convergence, and two approximate methods of log-likelihood ratios (LLR) estimation for channel decoding are given as well. Simulation results verify that the proposed algorithm outperforms the Neumann series expansion algorithms on both bit error ratio (BER) and computational complexity. It converges rapidly and achieves its performance quite close to that of the MMSE algorithm with only a small number of iterations.

Key words: massive MIMO, low-complicity, Kaczmarz, relaxation parameter, soft decision

中图分类号:

TN929.5

申滨, 赵书锋, 黄龙杨. 基于Kaczmarz迭代的大规模MIMO系统低复杂度软输出信号检测[J]. 电子学报, 2018, 46(11): 2746-2752.

SHEN Bin, ZHAO Shu-feng, HUANG Long-yang . Low-Complexity Soft-Output Signal Detection Based on Kaczmarz Method for Uplink Massive MIMO Systems[J]. Acta Electronica Sinica, 2018, 46(11): 2746-2752.

参考文献

[1] MARZETTA T L.Noncooperative cellular wireless with unlimited numbers of base station antennas[J].IEEE Transactions on Wireless Communications,2010,9(11):3590-3600.
[2] LU L,Li G Y,SWINDLEHURST A L,et al.An overview of massive MIMO:benefits and challenges[J].IEEE Journal of Selected Topics in Signal Processing,2014,8(5):742-758.
[3] LI Q,LI G,LEE W,et al.MIMO techniques in WiMAX and LTE:a feature overview[J].IEEE Communications Magazine,2010,48(5):86-92.
[4] WANG S,WANG C.Joint optimization of spectrum and energy efficiency in cognitive radio networks[J].Digital Communications & Networks,2015,1(3):161-170.
[5] NGO H Q,LARSSON E G,MARZETTA T L.Energy and spectral efficiency of very large multiuser MIMO systems[J].IEEE Transactions on Communications,2013,61(4):1436-1449.
[6] DAI L,WANG Z,YANG Z.Spectrally efficient time-frequency training OFDM for mobile large-scale MIMO systems[J].IEEE Journal on Selected Areas in Communications,2013,31(2):251-263.
[7] WU M,Yin B,VOSOUGHI A,et al.Approximate matrix inversion for high-throughput data detection in the large-scale MIMO uplink[A].IEEE International Symposium on Circuits and Systems[C].IEEE,2013.2155-2158.
[8] DATTA T,SRINIDHI N,CHOCKALINGAM A,et al.Random-restart reactive tabu search algorithm for detection in large-MIMO systems[J].IEEE Communications Letters,2010,14(12):1107-1109.
[9] PEREIRA A A J,SAMPAIO-NETO R.A random-list based LAS algorithm for near-optimal detection in large-scale uplink multiuser MIMO systems[A].Proceedings of 19th International ITG Workshop on Smart Antennas[C].VDE,2015.1-5.
[10] WU M,YIN B,WANG G,et al.Large-scale MIMO detection for 3GPP LTE:algorithms and FPGA implementations[J].IEEE Journal of Selected Topics in Signal Processing,2014,8(5):916-929.
[11] TANG C,LIU C,YUAN L,et al.High precision low complexity matrix inversion based on newton iteration for data detection in the massive MIMO[J].IEEE Communications Letters,2016,20(3):490-493.
[12] GAO X,DAI L,YUEN C,et al.Low-complexity MMSE signal detection based on richardson method for large-scale MIMO systems[A].Proceedings of 80th Vehicular Technology Conference (VTC2014-Fall)[C].IEEE,2014.1-5.
[13] DAI L,GAO X,SU X,et al.Low-complexity soft-output signal detection based on gauss-seidel method for uplink multiuser large-scale MIMO systems[J].IEEE Transactions on Vehicular Technology,2015,64(10):4839-4845.
[14] QIN X,YAN Z,HE G.A near-optimal detection scheme based on joint steepest descent and jacobi method for uplink massive MIMO systems[J].IEEE Communications Letters,2016,20(2):276-279.
[15] GAO X,DAI L,HU Y,et al.Matrix inversion-less signal detection using SOR method for uplink large-scale MIMO systems[A].Global Communications Conference[C].IEEE,2015.3291-3295.
[16] YIN B,WU M,CAVALLARO J R,et al.Conjugate gradient-based soft-output detection and precoding in massive MIMO systems[A].Global Communications Conference[C].IEEE,2014.3696-3701.
[17] XUE Y,ZHANG C,ZHANG S,et al.Steepest descent method based soft-output detection for massive MIMO uplink[A].2016 IEEE International Workshop on Signal Processing Systems (SiPS)[C].IEEE,2016.273-278.
[18] KACZMARZ S.Angenaherte auflosung von systemen linearer gleichungen[J].Bull.Internat Acad Polon Sci Lettres A,1937,35:355-357.
[19] PRASAD K N R S V,HOSSAIN E,BHARGAVA V K.Energy efficiency in massive MIMO-based 5G networks:opportunities and challenges[J].IEEE Wireless Communications,2017,24(3):86-94.
[20] SONG K,CHUNGUO L,HUANG Y,et al.Antenna selection for two-way full duplex massive MIMO networks with amplify-and-forward relay[J].Science China(Information Sciences),2017,60(2):022308.
[21] LIN Y,WANG Y,LI C,et al.Joint design of user association and power allocation with proportional fairness in massive MIMO HetNets[J].IEEE Access,2017,5(99):6560-6569.
[22] GORDON R,BENDER R,HERMAN G T.Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography[J].Journal of Theoretical Biology,1970,29(3):471-481.
[23] STROHMER T,VERSHYNIN R.A randomized kaczmarz algorithm with exponential convergence[J].Journal of Fourier Analysis and Applications,2009,15(2):262-278.
[24] NATTERER F.The Mathematics of Computerized Tomography[M].New York:Wiley,1986.107-108.
[25] NARASIMHAN T L,CHOCKALINGAM A.Channel hardening-exploiting message passing (CHEMP) receiver in large-scale MIMO systems[J].IEEE Journal of Selected Topics in Signal Processing,2014,8(5):847-860.

基于Kaczmarz迭代的大规模MIMO系统低复杂度软输出信号检测

Low-Complexity Soft-Output Signal Detection Based on Kaczmarz Method for Uplink Massive MIMO Systems

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]	廖勇, 李玉杰. 一种轻量化低复杂度的FDD大规模MIMO系统CSI反馈方法[J]. 电子学报, 2022, 50(5): 1211-1217.
[2]	何其恢, 朱立东. 一种基于双图案的卫星信号能量检测粗同步方法[J]. 电子学报, 2022, 50(3): 524-532.
[3]	廖勇, 杨馨怡, 杜洁汝. 基于两阶段的毫米波大规模MIMO低复杂度混合预编码算法[J]. 电子学报, 2021, 49(7): 1298-1304.
[4]	丁青锋, 刘梦霞. 基于混合精度ADC的大规模MIMO中继系统物理层安全性能研究[J]. 电子学报, 2021, 49(6): 1142-1150.
[5]	崔建华, 袁正道, 王忠勇, 路新华, 薛琦. 基于隐聚类和狄利特雷过程的大规模MIMO-OFDM接收机设计[J]. 电子学报, 2019, 47(12): 2515-2523.
[6]	雷维嘉, 王娟兵, 谢显中. 大规模MIMO中继系统中多用户物理层安全传输方案[J]. 电子学报, 2018, 46(12): 2878-2887.
[7]	门宏志, 刘文龙, 王楠, 金明录. 空间调制系统低复杂度的天线选择算法[J]. 电子学报, 2016, 44(6): 1322-1327.
[8]	熊竹林, 安建平. 一种低复杂度的低信噪比非相干直扩信号捕获算法[J]. 电子学报, 2016, 44(4): 753-760.
[9]	黄庆东;张林让;王纯;张波. 基于数据选择Householder多级维纳滤波导航接收机空时抗干扰方法[J]. 电子学报, 2011, 39(6): 1368-1372.
[10]	姜毅;张淑芳;胡青;孙晓文;张晶泊. 一种低复杂度GPS载波跟踪环路设计[J]. 电子学报, 2010, 38(12): 2822-2826.
[11]	李强, 雷霞, 李少谦. 基于一种新的SNR估计方法的切换迭代均衡[J]. 电子学报, 2007, 35(6A): 184-188.
[12]	江涛. 新的低复杂度降低OFDM信号峰均功率比的压缩扩张技术[J]. 电子学报, 2005, 33(7): 1218-1221.
[13]	王明曙, 陆建华, 周祖成. 一种新的应用于多载波码分多址系统的干扰对消算法[J]. 电子学报, 2005, 33(3): 507-510.
[14]	崔龙;凌聪;吴晓富. 基于改进型SOVA的Turbo译码[J]. 电子学报, 2001, 29(7): 923-926.
[15]	张中培;周亮;靳蕃. 低复杂度Turbo码译码并行实现[J]. 电子学报, 2001, 29(2): 272-274.