# On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems

arXiv: https://arxiv.org/abs/2107.08293 · HTML: https://mlsanity.com/paper/2107.08293/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 17 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Parameter Space Noise for Exploration](https://mlsanity.com/paper/1706.01905/) (2017)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [Beamforming Optimization for Intelligent Reflecting Surface with Discrete Phase Shifts](https://mlsanity.com/paper/1810.10718/) (2018)
- [Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications](https://mlsanity.com/paper/2005.11885/) (2020)
- [Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA](https://mlsanity.com/paper/2104.01414/) (2021)

## Built on by (most cited first, 2 of 2)

- [Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues](https://mlsanity.com/paper/2303.08106/) (2023)
- [Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments](https://mlsanity.com/paper/2211.09726/) (2022)

## Abstract (arXiv)

We consider an Intelligent Reflecting Surface (IRS)-aided multiple-input single-output (MISO) system for downlink transmission. We compare the performance of Deep Reinforcement Learning (DRL) and conventional optimization methods in finding optimal phase shifts of the IRS elements to maximize the user signal-to-noise (SNR) ratio. Furthermore, we evaluate the robustness of these methods to channel impairments and changes in the system. We demonstrate numerically that DRL solutions show more robustness to noisy channels and user mobility.