Sum Rate Maximization for Irregular Reconfigurable Intelligent Surface (RIS) Using Reinforcement Learning

Thimar Alsharify

doi:10.47134/jtsi.v3i1.5363

Authors

Thimar Falih Yasir Alsharify Wasit University

DOI:

https://doi.org/10.47134/jtsi.v3i1.5363

Keywords:

Radar-Communication Dual-Function System, 6th Generation, Reconfigurable Intelligent Surfaces, Data Rate, Sum Rate Maximization, Optimization, Reinforcement Learning

Abstract

Reconfigurable Intelligent Surfaces (RIS) is an innovative technology in telecommunications systems that use artificial and programmable surfaces to manage radio waves and optimize communication environments. This technology is particularly relevant in 5G and 6G systems as a tool to improve signal quality, reduce interference, and increase the capacity of communication systems. The signal-to-noise ratio (SNR) is very important in radar target detection. In this research, Reinforcement Learning Method used to optimize irregular reconfigurable intelligent surface. The reward function is optimized by adjusting the phase parameters of the arrays and pre-coding vectors. The reconfigurable intelligent surface is considered as irregular arrays. The method presented in this study is based on Sum rate Maximization. Reinforcement learning is used to find the optimal location of antennas. The results indicate the superiority of reinforcement learning over Tabu Optimization and greedy search methods.

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