Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications
This work addresses interference mitigation and signal enhancement in wireless communications, but it is incremental as it applies existing deep reinforcement learning methods to a specific domain.
The paper tackled the problem of optimizing network sum-rate in device-to-device communications using an intelligent reflecting surface by jointly optimizing transmit power and phase shifts, resulting in improved achievable rates and processing times as shown in simulations.
In this paper, we propose a deep reinforcement learning (DRL) approach for solving the optimisation problem of the network's sum-rate in device-to-device (D2D) communications supported by an intelligent reflecting surface (IRS). The IRS is deployed to mitigate the interference and enhance the signal between the D2D transmitter and the associated D2D receiver. Our objective is to jointly optimise the transmit power at the D2D transmitter and the phase shift matrix at the IRS to maximise the network sum-rate. We formulate a Markov decision process and then propose the proximal policy optimisation for solving the maximisation game. Simulation results show impressive performance in terms of the achievable rate and processing time.