TY - GEN
T1 - Efficient LLR Calculation for Uplink Coded Massive MIMO Systems
AU - Zhang, Meixiang
AU - Zhang, Zhi
AU - Kim, Sooyoung
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/16
Y1 - 2018/11/16
N2 - In the uplink massive MIMO systems, linear minimum mean square error (MMSE) algorithm can achieve near-optimal performance in combination with a soft iterative decoder, but suffers from high computational complexity due to the complicated matrix inversion. To approximate the performance of the classical MMSE detection algorithm, a number of iterative methods were proposed with reduced complexity by eliminating the matrix inversion. However, in order to apply these methods to coded systems with soft iterative decoders the post-equalization signal-to-interference-plus-noise ratio (PE-SINR) should be calculated in each layer to produce soft output values. In this paper, we propose to approximate the PE-SINR in each layer with a universal value calculated at the base station (BS), and apply symbol mapping techniques to the estimation of soft output in each layer to further reduce the computational complexity. The simulation results demonstrate that the detection algorithm with the proposed PE-SINR calculation approach achieves approximating performance to the conventional methods.
AB - In the uplink massive MIMO systems, linear minimum mean square error (MMSE) algorithm can achieve near-optimal performance in combination with a soft iterative decoder, but suffers from high computational complexity due to the complicated matrix inversion. To approximate the performance of the classical MMSE detection algorithm, a number of iterative methods were proposed with reduced complexity by eliminating the matrix inversion. However, in order to apply these methods to coded systems with soft iterative decoders the post-equalization signal-to-interference-plus-noise ratio (PE-SINR) should be calculated in each layer to produce soft output values. In this paper, we propose to approximate the PE-SINR in each layer with a universal value calculated at the base station (BS), and apply symbol mapping techniques to the estimation of soft output in each layer to further reduce the computational complexity. The simulation results demonstrate that the detection algorithm with the proposed PE-SINR calculation approach achieves approximating performance to the conventional methods.
KW - a posteriori SINR
KW - iterative methods
KW - massive MIMO
KW - MMSE
KW - post-equalization SINR
KW - soft-output detection
UR - https://www.scopus.com/pages/publications/85059482140
U2 - 10.1109/ICTC.2018.8539359
DO - 10.1109/ICTC.2018.8539359
M3 - Conference paper
AN - SCOPUS:85059482140
T3 - 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018
SP - 207
EP - 211
BT - 9th International Conference on Information and Communication Technology Convergence
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Information and Communication Technology Convergence, ICTC 2018
Y2 - 17 October 2018 through 19 October 2018
ER -