Reconfigurable Intelligent Surface Enhanced Device-to-Device Communications

TL;DR

This paper introduces a deep learning-based method to optimize reconfigurable intelligent surfaces for enhancing device-to-device communications, significantly improving network sum rate and quality of service.

Contribution

It presents a novel CNN-based deep Q-network approach for joint optimization of RIS placement and phase shifts in D2D networks, reducing complexity and outperforming benchmarks.

Findings

Achieves higher sum rate than benchmark algorithms.

Meets QoS requirements for D2D receivers and base station.

Demonstrates effectiveness of CNN-based DQN in RIS optimization.

Abstract

Reconfigurable intelligent surface (RIS) technology is a promising method to enhance the device-to-device (D2D) communications. To maximize the sum rate of the cellular and D2D networks, a joint optimization of the position and the phase shift of RIS in D2D communications is considered in this paper. To solve the non-convex sum rate maximum problem, we propose a novel convolutional neural network (CNN) based deep Q-network (DQN) that jointly optimizes the RIS position and its phase shift with lower complexity. Numerical results illustrate that the proposed algorithm can achieve higher sum rate compared to the benchmark algorithms, meanwhile meeting the quality of service (QoS) requirements at D2D receivers and the base station (BS).