大规模MIMO系统中基于二对角矩阵分解的低复杂度检测算法

曹海燕; 杨敬畏; 方昕; 许方敏

doi:10.11999/JEIT170498

大规模MIMO系统中基于二对角矩阵分解的低复杂度检测算法

doi: 10.11999/JEIT170498

基金项目:

国家自然科学基金(61501158, 61379027)，浙江省自然科学基金(LY14F010019, LQ15F01004)

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出版历程
- 收稿日期: 2017-05-24
- 修回日期: 2017-10-24
- 刊出日期: 2018-02-19

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

摘要: 在大规模多输入多输出(MIMO)系统的上行链路检测算法中，最小均方误差(MMSE)算法是接近最优的，但算法涉及到大矩阵求逆运算，计算复杂度仍然较高。近年提出的基于诺依曼级数近似的检测算法降低了复杂度但性能有一定的损失。为了降低复杂度的同时逼近MMSE算法性能，该文提出基于二对角矩阵分解的诺依曼级数(Neumann Series)近似，即将大矩阵分解为以两条主对角线上元素组成的矩阵与空心矩阵之和。理论分析与仿真结果表明所提算法检测性能逼近MMSE检测算法，且其复杂度从O(K3)降低到O(K2)，这里K是用户的数目。
- 大规模MIMO /
- 诺依曼级数 /
- 二对角矩阵 /
- 低复杂度
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.
- Massive MIMO /
- Neumann series /
- Two-diagonal matrix /
- Low complexity

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参考文献(13)

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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