TY - GEN
T1 - Gaussian mixture model-based Expectation-Maximization signal processing algorithm in power-efficiency networks
AU - Gong, Yi
AU - Meng, Fanke
AU - Li, Qingyu
AU - Yu, Keping
AU - Mumtaz, Shahid
AU - Muhaidat, Sami
N1 - Funding Information:
ACKNOWLEDGEMENTS This work is supported by Key Research cultivation project of Beijing Information Science and Technology University (2121YJPY224); foundation of Beijing Information Science and Technology University (2021XJJ24); horizontal Project of Beijing Information Science and Technology University (S2126116) and (S2126142).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Non-linear Multiple-Input Multiple-Output (MIMO) has attracted considerable attention because of its high power-efficiency characteristic, particularly in the fifth generation (5G) and beyond. This paper focuses on the non-linear MIMO baseband algorithms in power-efficiency networks. In previous works, Generalized Approximate Message Passing (GAMP) and importance sampling technique were used to solve the non-linear distortion in Halved Phase-Only (HPO-) MIMO system. However, its convergence rate becomes unstable, and it's converge is not guaranteed in some cases. In this paper, to improve the efficiency of convergence rate, we propose Gaussian Mixture Model (GMM) based ExpectationMaximization (EM) signal processing algorithm in HPO MIMO system. We first transforme channel estimation and multiuser detection problems into generalized linear mixed problems under π-phase observations. Then, the GMM algorithm is used to estimate the distribution of π-phase observation. Meanwhile, the EM algorithm is used to estimate the recovered signal. Simulation results show that the proposed method achieves high convergence and has better performance than the reference GAMP algorithm.
AB - Non-linear Multiple-Input Multiple-Output (MIMO) has attracted considerable attention because of its high power-efficiency characteristic, particularly in the fifth generation (5G) and beyond. This paper focuses on the non-linear MIMO baseband algorithms in power-efficiency networks. In previous works, Generalized Approximate Message Passing (GAMP) and importance sampling technique were used to solve the non-linear distortion in Halved Phase-Only (HPO-) MIMO system. However, its convergence rate becomes unstable, and it's converge is not guaranteed in some cases. In this paper, to improve the efficiency of convergence rate, we propose Gaussian Mixture Model (GMM) based ExpectationMaximization (EM) signal processing algorithm in HPO MIMO system. We first transforme channel estimation and multiuser detection problems into generalized linear mixed problems under π-phase observations. Then, the GMM algorithm is used to estimate the distribution of π-phase observation. Meanwhile, the EM algorithm is used to estimate the recovered signal. Simulation results show that the proposed method achieves high convergence and has better performance than the reference GAMP algorithm.
KW - channel estimation
KW - multi-user detection
KW - Non-linear MIMO
KW - power-efficiency networks
UR - http://www.scopus.com/inward/record.url?scp=85137266185&partnerID=8YFLogxK
U2 - 10.1109/ICC45855.2022.9838814
DO - 10.1109/ICC45855.2022.9838814
M3 - Conference contribution
AN - SCOPUS:85137266185
T3 - IEEE International Conference on Communications
SP - 1028
EP - 1033
BT - ICC 2022 - IEEE International Conference on Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Communications, ICC 2022
Y2 - 16 May 2022 through 20 May 2022
ER -