# Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications

arXiv: https://arxiv.org/abs/2108.02892 · HTML: https://mlsanity.com/paper/2108.02892/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 6 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning](https://mlsanity.com/paper/2106.03129/) (2021)
- [Optimizing Age of Information Through Aerial Reconfigurable Intelligent Surfaces: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2011.04817/) (2020)
- [Reconfigurable Intelligent Surface-assisted Multi-UAV Networks: Efficient Resource Allocation with Deep Reinforcement Learning](https://mlsanity.com/paper/2105.14142/) (2021)
- [Reconfigurable Intelligent Surface Assisted Device-to-Device Communications](https://mlsanity.com/paper/2007.00859/) (2020)
- [Reconfigurable Intelligent Surface (RIS)-Enhanced Two-Way OFDM Communications](https://mlsanity.com/paper/2005.01910/) (2020)
- [Reconfigurable Intelligent Surfaces for Energy Efficiency in D2D Communication Network](https://mlsanity.com/paper/2006.10320/) (2020)

## Abstract (arXiv)

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.