TY - GEN
T1 - Multiuser detection in noise enhanced eigenvector subspace for large scale MIMO communications
AU - Jiang, Xiaolin
AU - Zheng, Liming
AU - Wang, Gang
AU - Yang, Wenchao
AU - Wang, Jinlong
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/6/22
Y1 - 2016/6/22
N2 - This paper proposes a signal detection algorithm with good performance in the large scale uplink multiuser multiple-input multiple-output (MU-MIMO) systems. The proposed algorithm employs the minimum mean-square error (MMSE) detection result as the initial values, and project random noise to the orthonormal eigenvector subspace to amend the error of the noise enhancement of the MMSE detection, where the noise components become uncorrelated. To reduce the complexity, an approximated log likelihood function is employed, and only fixed number of candidates with small approximated log likelihood function values are used for further calculation. Then the detected signals are quantized and selected that minimize the log likelihood function. As the noise projected to each eigenvector is uncorrelated each other, the MU-MIMO detection algorithm is expected to achieve good performance. Computer simulations show that in a 128×64 uplink multiuser MIMO system, the BER performance of the proposed algorithm is superior to MMSE-SIC, while costing only a fraction of the complexity compared with MMSE-SIC.
AB - This paper proposes a signal detection algorithm with good performance in the large scale uplink multiuser multiple-input multiple-output (MU-MIMO) systems. The proposed algorithm employs the minimum mean-square error (MMSE) detection result as the initial values, and project random noise to the orthonormal eigenvector subspace to amend the error of the noise enhancement of the MMSE detection, where the noise components become uncorrelated. To reduce the complexity, an approximated log likelihood function is employed, and only fixed number of candidates with small approximated log likelihood function values are used for further calculation. Then the detected signals are quantized and selected that minimize the log likelihood function. As the noise projected to each eigenvector is uncorrelated each other, the MU-MIMO detection algorithm is expected to achieve good performance. Computer simulations show that in a 128×64 uplink multiuser MIMO system, the BER performance of the proposed algorithm is superior to MMSE-SIC, while costing only a fraction of the complexity compared with MMSE-SIC.
KW - Eigenvector Subspace Search
KW - Large Scale MIMO
KW - MU-MIMO
KW - Multiuser Detection
UR - https://www.scopus.com/pages/publications/84980359721
U2 - 10.1109/CHINACOM.2015.7497968
DO - 10.1109/CHINACOM.2015.7497968
M3 - 会议稿件
AN - SCOPUS:84980359721
T3 - Proceedings of the 2015 10th International Conference on Communications and Networking in China, CHINACOM 2015
SP - 371
EP - 376
BT - Proceedings of the 2015 10th International Conference on Communications and Networking in China, CHINACOM 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Communications and Networking in China, CHINACOM 2015
Y2 - 15 August 2015 through 17 August 2015
ER -