# A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems

arXiv: https://arxiv.org/abs/2302.14706 · HTML: https://mlsanity.com/paper/2302.14706/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 24 Jan 2023.

## Builds on (most cited first, 9 of 9)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [Reconfigurable Intelligent Surfaces: Principles and Opportunities](https://mlsanity.com/paper/2007.03435/) (2020)
- [Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications](https://mlsanity.com/paper/2002.12271/) (2020)
- [An Overview of Signal Processing Techniques for RIS/IRS-aided Wireless Systems](https://mlsanity.com/paper/2112.05989/) (2021)
- [Intelligent Reflecting Surface Enhanced Wireless Network: Joint Active and Passive Beamforming Design](https://mlsanity.com/paper/1809.01423/) (2018)
- [Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks](https://mlsanity.com/paper/2008.12938/) (2020)
- [Beamforming in Integrated Sensing and Communication Systems with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2206.07679/) (2022)
- [Swarm of UAVs for Network Management in 6G: A Technical Review](https://mlsanity.com/paper/2210.03234/) (2022)

## Abstract (arXiv)

This letter investigates an intelligent reflecting surfaces (IRS)-enhanced network from spectral efficiency enhancement point of view for downlink multi-user (MU) multi-input-single-output systems (MISO). In contrast to previous works which mainly focused on alternative optimization methods, we investigate the non-convex joint optimization problem of the active transmit beamforming matrix at the base station together with the passive phase shift matrix at the IRS by utilizing two deep reinforcement learning frameworks, i. e., deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3). Simulation results reveal that the neural networks in the latter scheme perform generally more satisfactorily in various situations.