Evolutionary programming: a population-based machine learning model for coded MIMO channels

Xingming Li; Jigang Yu; Zhiliang Qin; Yuanhao Sun; Qidong Lu; Xiaowei Liu

doi:10.1117/12.2588917

20 January 2021 Evolutionary programming: a population-based machine learning model for coded MIMO channels

Xingming Li, Jigang Yu, Zhiliang Qin, Yuanhao Sun, Qidong Lu, Xiaowei Liu

Proceedings Volume 11719, Twelfth International Conference on Signal Processing Systems; 117190W (2021) https://doi.org/10.1117/12.2588917
Event: Twelfth International Conference on Signal Processing Systems, 2020, Shanghai, China

Abstract

The conventional channel detector based on the maximum a posteriori (MAP) algorithm for coded multiple-input multiple-output (MIMO) multiuser systems has a computational complexity growing exponentially with the product of the number of users, the number of transmit antennas, and the symbol constellation size. In this paper, we consider the multiuser detection problem from a combinatorial optimization viewpoint and develop a low-complexity iterative receiver based on the evolutionary programming (EP) technique. Simulation results show that with the proposed receiver, the performance of coded multiuser systems approaches that of the iteratively MAP-decoded single-user (SU) MIMO system at a significantly reduced computational complexity even for unknown channel scenarios.

Citation Download Citation

Xingming Li, Jigang Yu, Zhiliang Qin, Yuanhao Sun, Qidong Lu, and Xiaowei Liu "Evolutionary programming: a population-based machine learning model for coded MIMO channels", Proc. SPIE 11719, Twelfth International Conference on Signal Processing Systems, 117190W (20 January 2021); https://doi.org/10.1117/12.2588917

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