Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems

• Published in: IEEE transactions on wireless communications Vol. 20; no. 2; pp. 1394 - 1410
• Main Authors: Hu, Qiyu, Cai, Yunlong, Shi, Qingjiang, Xu, Kaidi, Yu, Guanding, Ding, Zhi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; Artificial neural networks; Back propagation; Back propagation networks; Communications systems; Complexity; Computational complexity; Deep-unfolding neural network; Design optimization; Iterative algorithms; Iterative methods; machine learning; MIMO (control systems); multiuser MIMO; Neural networks; Optimization; Precoding; precoding design; weighted MMSE
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2020.3033334

Optimization theory assisted algorithms have received great attention for precoding design in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant optimization algorithms are able to provide excellent performance, they generally require considerable computational complexity, which gets in the way of their practical application in real-time systems. In this work, in order to address this issue, we first propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed in matrix form to better solve the problems in communication systems. Then, we implement the proposed deep-unfolding framework to solve the sum-rate maximization problem for precoding design in MU-MIMO systems. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. Specifically, the iterative WMMSE algorithm is unfolded into a layer-wise structure, where a number of trainable parameters are introduced to replace the high-complexity operations in the forward propagation. To train the network, a generalized chain rule of the IAIDNN is proposed to depict the recurrence relation of gradients between two adjacent layers in the back propagation. Moreover, we discuss the computational complexity and generalization ability of the proposed scheme. Simulation results show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.