TY - GEN
T1 - Efficient iterative soft detection based on polynomial approximation for massive MIMO
AU - Wang, Feng
AU - Zhang, Chuan
AU - Liang, Xiao
AU - Wu, Zhizheng
AU - Xu, Shugong
AU - You, Xiaohu
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/30
Y1 - 2015/11/30
N2 - In massive multiple-input multiple-output (MIMO) systems, linear minimum mean square error (MMSE) detection is near-optimal but involves large dimensional matrix inversion, which results in high complexity. To this end, Neumann series expansion (NSE) approximation, which avoids the direct computation of the matrix inversion, is recently investigated due to its low implementation complexity. Unfortunately, the complexity reduction can only be achieved well when the required number of the NSE terms L is small. To solve this problem, we proposed an iterative NSE (INSE) algorithm for MMSE detection at a manageable complexity even for large L. An approximation method based on NSE is proposed to compute the log-likelihood ratios (LLRs) for channel decoders. Both analytical and numerical results have demonstrated that, the overall complexity of the proposed soft-output MMSE-INSE algorithm is significantly reduced compared with the conventional NSE method and the Cholesky decomposition method, while keeping similar detection performance.
AB - In massive multiple-input multiple-output (MIMO) systems, linear minimum mean square error (MMSE) detection is near-optimal but involves large dimensional matrix inversion, which results in high complexity. To this end, Neumann series expansion (NSE) approximation, which avoids the direct computation of the matrix inversion, is recently investigated due to its low implementation complexity. Unfortunately, the complexity reduction can only be achieved well when the required number of the NSE terms L is small. To solve this problem, we proposed an iterative NSE (INSE) algorithm for MMSE detection at a manageable complexity even for large L. An approximation method based on NSE is proposed to compute the log-likelihood ratios (LLRs) for channel decoders. Both analytical and numerical results have demonstrated that, the overall complexity of the proposed soft-output MMSE-INSE algorithm is significantly reduced compared with the conventional NSE method and the Cholesky decomposition method, while keeping similar detection performance.
KW - approximate matrix inversion
KW - Massive MIMO
KW - MMSE detection
KW - Neumann series
KW - soft-output decision
UR - http://www.scopus.com/inward/record.url?scp=84975689879&partnerID=8YFLogxK
U2 - 10.1109/WCSP.2015.7341297
DO - 10.1109/WCSP.2015.7341297
M3 - Conference Proceeding
AN - SCOPUS:84975689879
T3 - 2015 International Conference on Wireless Communications and Signal Processing, WCSP 2015
BT - 2015 International Conference on Wireless Communications and Signal Processing, WCSP 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Conference on Wireless Communications and Signal Processing, WCSP 2015
Y2 - 15 October 2015 through 17 October 2015
ER -