PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
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.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Xingming Li, Jigang Yu, Zhiliang Qin, Yuanhao Sun, Qidong Lu, 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