Machine Learning-Based Hybrid Precoding With Low-Resolution Analog Phase Shifters

• Published in: IEEE communications letters Vol. 25; no. 1; pp. 186 - 190
• Main Authors: Zhang, Yu, Dong, Xiaodai, Zhang, Zhi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; cross-entropy; Entropy (Information theory); Hybrid power systems; hybrid precoding; Iterative methods; Machine learning; Matching pursuit algorithms; Millimeter waves; MIMO; MIMO (control systems); MIMO communication; mmWave communications; Optimization; Optimized production technology; Phase shifters; Precoding; Probability distribution; Radio frequency
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2020.3025025

In this letter, inspired by the cross-entropy (CE) optimization in machine learning, we propose a CE-based algorithm for hybrid precoding with low-resolution analog phase shifters in millimeter wave (mmWave) multi-input multi-output (MIMO) systems. The main idea is to generate some candidate analog precoders according to a series of pre-defined probability distributions and select partial analog precoders as elites to update the probability distributions. Through iteration, the probability distributions will converge to a stable state and the optimal precoders can be obtained with a sufficiently high probability. Furthermore, we extend the proposed algorithm to multi-user hybrid precoding. The simulation results demonstrate that the proposed algorithm can achieve near-optimal performance of the fully-digital precoding with lower computational complexity than other near-optimal algorithms.