Deep Reinforcement Learning Based Intelligent Reflecting Surface Optimization for MISO Communication Systems

By Keming Feng, Qisheng Wang, Xiao Li, Chao-Kai Wen. IEEE Wireless Communications Letters, 2020

324 citations recorded by the source corpus.

This letter investigates the intelligent reflecting surface (IRS)-aided multiple-input single-output wireless transmission system. Particularly, the optimization of the passive phase shift of each element at IRS to maximize the downlink received signal-to-noise ratio is considered. Inspired by the huge success of deep reinforcement learning (DRL) on resolving complicated control problems, we develop a DRL based framework to solve this non-convex optimization problem. Numerical results reveal that the proposed DRL based framework can achieve almost the upper bound of the received SNR with relatively low time consumption.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/3bece1ce0eda327c3e2b61d28131d54fa371261a

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Citations in the corpus

Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges (2021, background): “For example, in [45], deep reinforcement learning is considered to maximize the downlink received signal to noise ratio (SNR) in IRS-aided communication system.”
Reconfigurable Intelligent Surfaces: Principles and Opportunities (2020, methodology): “To fully reap the benefits of the RIS in wireless networks, the joint active and passive design of the RIS-enhanced system has been considered in MISO system [127], [128], OFDM-based system [129], wireless security system [130] and millimeter wave system [131] with the aid of reinforcement learning ”
Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead (2020, methodology): “In [271], the authors optimize the design of the phase shifts of an RIS-aided downlink MISO wireless communication system, with the goal of maximizing the received signal-to-noise ratio.”
Modeling and Architecture Design of Reconfigurable Intelligent Surfaces Using Scattering Parameter Network Analysis (2020, methodology): “Whilethe vast majority of research on RIS has been devoted to system level optimization [7]-[30], developing models that satisfy the necessary electromagnetic (EM) equations while providing tractable and useful RIS aided communication models still remains an open problem.”
Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial (2020, methodology): “[70] SU MISO Power minimization Deep learning Instantaneous CSI”
Deep Reinforcement Learning-Based Intelligent Reflecting Surface for Secure Wireless Communications (2020, background): “the authors in [28] and [29] proposed deep reinforcement learning (DRL) based approach to address the non-convex optimization problem, and the phase shifts at the IRS are optimized effectively.”
Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A Contemporary Survey (2019, background): “The authors in [176] propose a novel deep reinforcement learning (DRL) approach to address the secrecy rate maximization problem.”
Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead (2020, background): “In [273], the authors optimize the design of the phase”