Reconfigurable Intelligent Surface Assisted Device-to-Device Communications

TL;DR

This paper explores the use of reconfigurable intelligent surfaces (RIS) to improve device-to-device (D2D) communications by optimizing the environment through a novel deep reinforcement learning approach, leading to higher uplink rates.

Contribution

It introduces a new method that treats the wireless environment as a variable and employs a double deep Q-network to optimize RIS configuration for D2D networks.

Findings

Achieves higher uplink rates than benchmarks.

Maintains QoS for BS and D2D receivers.

Offers a robust and low-complexity solution.

Abstract

Reconfigurable intelligent surface (RIS) technology is a promising method to enhance wireless communications services and to realize the smart radio environment. In this paper, we investigate the application of RIS in D2D communications, and maximize the sum of the transmission rate of the D2D underlaying networks in a new perspective. Instead of solving similarly formulated resource allocation problems for D2D communications, this paper treats the wireless environment as a variable by adjusting the position and phase shift of the RIS. To solve this non-convex problem, we propose a novel double deep Q-network (DDQN) based structure which is able to achieve the near-optimal performance with lower complexity and enhanced robustness. Simulation results illustrate that the proposed DDQN based structure can achieve a higher uplink rate compared to the benchmarks, meanwhile meeting the quality of service (QoS) requirements at the base station (BS) and D2D receivers.