Unlocking Metasurface Practicality for B5G Networks: AI-assisted RIS Planning
This work addresses the practical deployment challenges of RISs for B5G networks, offering incremental improvements in coverage and efficiency for wireless network designers.
The paper tackles the problem of deploying reconfigurable intelligent surfaces (RISs) in areas with poor coverage for B5G networks by introducing D-RISA, a deep reinforcement learning solution that achieves a 10-dB increase in minimum SNR and reduces computational time by up to 25% compared to state-of-the-art methods.
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.