# Unlocking Metasurface Practicality for B5G Networks: AI-assisted RIS Planning

arXiv: https://arxiv.org/abs/2310.10330 · HTML: https://mlsanity.com/paper/2310.10330/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Reconfigurable Intelligent Surfaces: Principles and Opportunities](https://mlsanity.com/paper/2007.03435/) (2020)
- [Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-user Communications: Achievable Rates with Limited Discrete Phase Shifts](https://mlsanity.com/paper/1910.14328/) (2019)
- [Reconfigurable Intelligent Surfaces: Three Myths and Two Critical Questions](https://mlsanity.com/paper/2006.03377/) (2020)
- [DRAG: Deep Reinforcement Learning Based Base Station Activation in Heterogeneous Networks](https://mlsanity.com/paper/1809.02159/) (2018)
- [Reinforcement Learning of Beam Codebooks in Millimeter Wave and Terahertz MIMO Systems](https://mlsanity.com/paper/2102.11392/) (2021)
- [Aerial Intelligent Reflecting Surface: Joint Placement and Passive Beamforming Design with 3D Beam Flattening](https://mlsanity.com/paper/2007.13295/) (2020)
- [Fueling the Next Quantum Leap in Cellular Networks: Embracing AI in 5G Evolution towards 6G](https://mlsanity.com/paper/2111.10663/) (2021)
- [Designing, Building, and Characterizing RF Switch-based Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2207.07121/) (2022)
- [Planning Mm-Wave Access Networks With Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2105.11755/) (2021)
- [MARISA: A Self-configuring Metasurfaces Absorption and Reflection Solution Towards 6G](https://mlsanity.com/paper/2112.01949/) (2021)
- [LOKO: Localization-aware Roll-out Planning for Future Mobile Networks](https://mlsanity.com/paper/2201.04051/) (2022)
- [RIS-Aware Indoor Network Planning: The Rennes Railway Station Case](https://mlsanity.com/paper/2201.07591/) (2022)
- [Towards a Heterogeneous Smart Electromagnetic Environment for Millimeter-Wave Communications: An Industrial Viewpoint](https://mlsanity.com/paper/2202.12194/) (2022)
- [SARIS: Scattering Aware Reconfigurable Intelligent Surface model and Optimization for Complex Propagation Channels](https://mlsanity.com/paper/2302.01739/) (2023)

## Abstract (arXiv)

The advent of reconfigurable intelligent surfaces(RISs) brings along significant improvements for wireless technology on the verge of beyond-fifth-generation networks (B5G).The proven flexibility in influencing the propagation environment opens up the possibility of programmatically altering the wireless channel to the advantage of network designers, enabling the exploitation of higher-frequency bands for superior throughput overcoming the challenging electromagnetic (EM) propagation properties at these frequency bands. However, RISs are not magic bullets. Their employment comes with significant complexity, requiring ad-hoc deployments and management operations to come to fruition. In this paper, we tackle the open problem of bringing RISs to the field, focusing on areas with little or no coverage. In fact, we present a first-of-its-kind deep reinforcement learning (DRL) solution, dubbed as D-RISA, which trains a DRL agent and, in turn, obtain san optimal RIS deployment. We validate our framework in the indoor scenario of the Rennes railway station in France, assessing the performance of our algorithm against state-of-the-art (SOA) approaches. Our benchmarks showcase better coverage, i.e., 10-dB increase in minimum signal-to-noise ratio (SNR), at lower computational time (up to -25 percent) while improving scalability towards denser network deployments.