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大規(guī)模MIMO系統(tǒng)中基于二對(duì)角矩陣分解的低復(fù)雜度檢測(cè)算法

曹海燕,  楊敬畏,  方昕,  許方敏

曹海燕, 楊敬畏, 方昕, 許方敏. 大規(guī)模MIMO系統(tǒng)中基于二對(duì)角矩陣分解的低復(fù)雜度檢測(cè)算法[J]. 電子與信息學(xué)報(bào), 2018, 40(2): 416-420. doi: 10.11999/JEIT170498
引用本文: 曹海燕, 楊敬畏, 方昕, 許方敏. 大規(guī)模MIMO系統(tǒng)中基于二對(duì)角矩陣分解的低復(fù)雜度檢測(cè)算法[J]. 電子與信息學(xué)報(bào), 2018, 40(2): 416-420. doi: 10.11999/JEIT170498
CAO Haiyan, YANG Jingwei, FANG Xin, XU Fangmin. Low Complexity Detection Algorithm Based on Two-diagonal Matrix Decomposition in Massive MIMO Systems[J]. Journal of Electronics & Information Technology, 2018, 40(2): 416-420. doi: 10.11999/JEIT170498
Citation: CAO Haiyan, YANG Jingwei, FANG Xin, XU Fangmin. Low Complexity Detection Algorithm Based on Two-diagonal Matrix Decomposition in Massive MIMO Systems[J]. Journal of Electronics & Information Technology, 2018, 40(2): 416-420. doi: 10.11999/JEIT170498

大規(guī)模MIMO系統(tǒng)中基于二對(duì)角矩陣分解的低復(fù)雜度檢測(cè)算法

doi: 10.11999/JEIT170498 cstr: 32379.14.JEIT170498
基金項(xiàng)目: 

國(guó)家自然科學(xué)基金(61501158, 61379027)，浙江省自然科學(xué)基金(LY14F010019, LQ15F01004)

Low Complexity Detection Algorithm Based on Two-diagonal Matrix Decomposition in Massive MIMO Systems

Funds: 

The National Natural Science Foundation of China (61501158, 61379027), The Natural Science Foundation of Zhejiang Province (LY14F010019, LQ15F01004)

  • 摘要: 在大規(guī)模多輸入多輸出(MIMO)系統(tǒng)的上行鏈路檢測(cè)算法中，最小均方誤差(MMSE)算法是接近最優(yōu)的，但算法涉及到大矩陣求逆運(yùn)算，計(jì)算復(fù)雜度仍然較高。近年提出的基于諾依曼級(jí)數(shù)近似的檢測(cè)算法降低了復(fù)雜度但性能有一定的損失。為了降低復(fù)雜度的同時(shí)逼近MMSE算法性能，該文提出基于二對(duì)角矩陣分解的諾依曼級(jí)數(shù)(Neumann Series)近似，即將大矩陣分解為以兩條主對(duì)角線上元素組成的矩陣與空心矩陣之和。理論分析與仿真結(jié)果表明所提算法檢測(cè)性能逼近MMSE檢測(cè)算法，且其復(fù)雜度從O(K3)降低到O(K2)，這里K是用戶的數(shù)目。
    Abstract: Minimum Mean Square Error (MMSE) algorithm is near-optimal for uplink massive MIMO systems, but it involves high-complexity matrix inversion. Recently, the proposed detection algorithm based on Neumann series approximation reduces the complexity with some performance losses. In order to reduce the complexity while approaching the performance of MMSE algorithm, the Neumann series approximation based on two-diagonal matrix decomposition is proposed in this paper, that is, the large matrix is decomposed into the sum of the two elements of the main diagonal and the hollow matrix. The theoretical analysis and simulation results show that the detection performance of the proposed algorithm is close to the MMSE detection algorithm while its computational complexity is reduced from O(K3)toO(K2), where K is the number of users.
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  • ANDREWA J G, BUZZI S, WAN C, et al. What will 5G be?[J]. IEEE Journal on Selected Areas in Communications, 2014, 32(6): 1065-1082. doi: 10.1109/JSAC.2014.2328098.
    MARZETTA T L. Noncooperative cellular wireless with unlimited numbers of base station antennas[J]. IEEE Transactions on Wireless Communications, 2010, 9(11): 3590-3600. doi: 10.1109/TWC.2010.092810.091092.
    NGO H Q, LARSSON E G, and MARZETTA T L. Energy and spectral efficiency of very large multiuser MIMO systems [J]. IEEE Transactions on Communications, 2013, 61(4): 1436-1449. doi: 10.1109/TCOMM.2013.020413.110848.
    RUSEK F, PERSSON D, LAU B K, et al. Scaling up MIMO: Opportunities and challenges with very large arrays[J]. IEEE Signal Processing Magazine, 2012, 30(1): 40-60. doi: 10.1109/ MSP.2011.2178495.
    VIVONE G and BRACA P. Joint probabilistic data association tracker for extended target tracking applied to X-band marine radar data[J]. IEEE Journal of Oceanic Engineering, 2016, 41(4): 1007-1019. doi: 10.1109/JOE.2015. 2503499.
    YUAN G, HAN N, and KAISER T. Massive MIMO detection based on belief propagation in spatially correlated channels[C]. International Itg Conference on Systems, Communications and Coding, Hamburg, Germany, 2017: 1-6.
    YIN B, WU M, STYDER C, et al. Implementation trade-offs for linear detection in large-scale MIMO systems[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, 2013: 2679-2683. doi: 10.1109/ ICASSP.2013.6638142.
    HOCHWALD B M, MARZETTA T L, and TAROKH V. Multiple-antenna channel hardening and its implications for rate feedback and scheduling[J]. IEEE Transactions on Information Theory, 2004, 50(9): 1893-1909. doi: 10.1109/ TIT.2004.833345.
    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. doi: 10.1109/ LCOMM.2015.2514281.
    NING J, LU Z, XIE T, et al. Low complexity signal detector based on SSOR method for massive MIMO systems[C]. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, Ghent, 2015: 1-4. doi: 10.1109/BMSB. 2015.7177185.
    GAZZAH H. Low-complexity delay-controlled blind MMSE/ ZF multichannel equalization[C]. IEEE GCC Conference and Exhibition, Dubai, 2011: 100-103. doi: 10.1109/IEEEGCC. 2011.5752472.
    WU M, YIN B, VOSOUGHI A, et al. Approximate matrix inversion for high-throughput data detection in the large-scale MIMO uplink[C]. IEEE International Symposium on Circuits and Systems, Beijing, 2013: 2155-2158.
    VORST H A V D. An iterative solution method for solving f (A) x=b, using Krylov subspace information obtained for the symmetric positive definite matrix A[J]. Journal of Computational and Applied Mathematics, 1987, 18(2): 249-263.
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出版歷程
  • 收稿日期:  2017-05-24
  • 修回日期:  2017-10-24
  • 刊出日期:  2018-02-19

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