Machine Learning-Inspired Algorithmic Framework for Intelligent Reflecting Surface-Assisted Wireless Systems

This study considers the simultaneous optimization problem of the transmit beamforming at the access point and the reflecting beamforming at the intelligent reflecting surface (IRS) in an IRS-assisted multiuser downlink system. This joint optimization problem is nonconvex and challenging due to the intricate coupling of transmit and reflecting beamforming variables and the highly nonconvex constant modulus constraints. All the known solutions apply the alternating optimization framework to decouple the joint optimization problem into two problems. Then, these problems are optimized individually with different optimization methods in an alternating manner. However, such an alternative procedure may cause performance degradation. On the basis of the cross-entropy (CE) framework, which was developed initially for machine learning applications, we propose a machine learning-inspired algorithmic framework for
simultaneously
optimizing transmit and reflecting beamforming in an IRS-assisted wireless system. Extensive simulation results reveal that the proposed CE-based algorithms significantly perform better than the state-of-the-art test algorithms for various system configurations.