基于近似信息传递算法的大规模MIMO信号检测

doi:10.11959/j.issn.1000-0801.2016218

摘要/Abstract

摘要：

大规模多输入多输出(MIMO)技术通过增加天线的数目可以有效降低发送功率，提高能量效率，被认为是5G移动通信的一项关键技术。随着天线数目的大幅增加，信号检测的复杂度随之增加。分析了大规模MIMO 信号检测的研究现状，提出了近似信息传递(AMP)算法，并比较了 AMP 算法、Richarson 算法以及Neumann级数迭代近似算法的复杂度。仿真结果表明，该算法使用较少的迭代次数即可达到和MMSE近似的系统差错性能。

关键词: 大规模MIMO, 信号检测, 近似信息传递算法, MMSE, 误码率

Abstract:

Massive multiple-input multiple-output(MIMO)brings huge improvements in energy efficiency and reduces emission power by using a large number of antennas,which is known as a key technology in the fifth generation (5G)mobile communication.With the increasing number of antennas,the complexity of signal detection is increasing at the same time.According to the research status of massive MIMO signal detection,approximate message passing algorithm was proposed.The complexity of AMP was compared to some iteration algorithm like Richarson algorithm and Neumann series approximation algorithm.The results indicate that the proposed algorithm can use less iteration to achieve almost the same performance of MMSE algorithm.

Key words: massiveMIMO, signaldetection, approximatemessagepassingalgorithm, MMSE, errorbitrate

秦闯,郑紫微,娄达平,富显祖. 基于近似信息传递算法的大规模MIMO信号检测[J]. 电信科学, 2016, 32(9): 16-21.

Chuang QIN,Ziwei ZHENG,Daping LOU,Xianzu FU. Massive MIMO signal detection based on approximate message passing algorithm[J]. Telecommunications Science, 2016, 32(9): 16-21.

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参考文献 20

[1]	SORENSEN L , SKOUBY K E . User scenarios 2020[R]. [S.l.:s.n], 2009.
[2]	3GPP. Physical channels and modulation (Release 11):TS36.211[S]. 2010.
[3]	MARZETTA T L . Noncooperative cellular wireless with unlimited numbers of base station antennas[J]. IEEE Transactions on Wireless Communications, 2010,9(11): 3590-3600.
[4]	NGO H Q , LARSSON E G , MARZETTA T L . Energy and spectral efficiency of very large multiuser MIMO system[J]. IEEE Transactions on Wireless Communications, 2013,61(4): 1436-1449.
[5]	HEATH RW , LOZANO A , MARZETTA T L ,et al. Five disruptive technology directions for 5G[J]. IEEE Communications Magazine, 2013,52(2): 74-80.
[6]	LARSSON E G , TUFVESSON F , EDFORS O ,et al. Massive MIMO for next generation wireless systems[J]. IEEE Journal of Selected Topics in Signal Processing, 2014,8(5): 742-758.
[7]	尤肖虎, 潘志文, 高西奇 ,等. 5G移动通信发展趋势与若干关键技术[J]. 中国科学, 2014,44(5): 551-563. YOU X H , PAN Z W , GAO X Q ,et al. 5G mobile communication development trend and some key technology[J]. Science China, 2014,44(5): 551-563.
[8]	SRINIDHI N , DATTA T , CHOCKALINGAM A ,et al. Layered tabu search algorithm for large-MIMO detection and a lower bound on ML performance[J]. IEEE Transactions on Wireless Communications, 2011,59(11): 2955-2963.
[9]	SOM P , DATTA T , SRINIDHI N ,et al. Low-complexity detection in large-dimension MIMO-ISI channels using graphical models[J]. IEEE Journal of Selected Topics in Signal Processing, 2011,5(8): 1497-1511.
[10]	STUDER C,B?LCSKEI H . Soft-input soft-output single tree-search sphere decoding[J]. IEEE Transactions on Information Theory, 2010,56(10): 4827-4842.
[11]	LARSSON E,JALDéN J . Fixed-complexity soft MIMO detection via partial marginalization[J]. IEEE Transactions on Signal Processing, 2008,56(8): 3397-3407.
[12]	STUDER C , FATEH S , SEETHALER D . ASIC implementation of soft-input soft-output MIMO detection using MMSE parallel interference cancellation[J]. IEEE Journal of Solid-State Circuits, 2011,46(7): 1754-1765.
[13]	YIN B , WU M , STUDER C ,et al. Implementation trade-offs for linear detection in large-scale MIMO systems[C]// IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP’13),May 26-31,2013,Vancouver,USA. New Jersey:IEEE Press, 2013: 2679-2683.
[14]	DAI L , GAO X , SU X ,et al. Low-complexity soft-output signal detection based on Gauss-Seidel method for uplink multi-user large-scale MIMO systems[J]. IEEE Transactions on Vehicular Technology, 2015,64(10): 4839-4845.
[15]	GAO X , DAI L , MA Y ,et al. Low-complexity near-optimal signal detection for uplink large-scale MIMO systems[J]. Electronics Letters, 2014,50(18): 1326-1328.
[16]	SOM P , DATTA T , CHOCKALINGAM A ,et al. Improved large MIMO detection based on damped belief propagation[J]. IEEE Information Theory Workshop(ITW), 2010: 1-5.
[17]	陈昱妏 . 利用近似讯息传递算法于大规模多天线系统解调器之研究[D]. 台北;中山大学通讯工程研究所, 2015: 1-81. CHEN Y W . Research on messages using approximate algorithm in large-scale antenna system of modem[D]. Taipei:Institute of Communication Engineering,Sun Yat-sen University, 2015: 1-81.
[18]	ADERSON M R . Sparse inference using approximate message passing[D]. Copenhagen:Technical University of Denmark,thesis, 2014.
[19]	WU S , KUANG L , NI Z ,et al. Low-complexity iterative detection for large-scale multiuser MIMO-OFDM systems using approximate message passing[J]. IEEE Journal of Selected Topics in Signal Processing, 2014,8(5): 902-915.
[20]	CHEN L . Iterative soft decoding of reed-solomon convolutional concatenated codes[J]. IEEE Transactions on Communications, 2013,11(10): 4076-408.

MMSE	迭代次数	Neumann级数近似方法	Richardson方法	本文提出的AMP方法
O(M³)	i=2	8M²-4M	8M²+4M	18MN+16MK+8N+4M
	i=3	8M³+16M²-10M	12M²+6M	27MN+24MK+12N+6M
	i=4	16M³-4M²+4M	16M²+8M	36MN+32MK+16N+8M
	i=5	24M³-16M²+6M	20M²+10M	45MN+40MK+20N+10M