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.

该论文介绍了可重构智能表面（RIS）的应用，重点在于解决无覆盖区域的问题，并提出了一种基于深度强化学习的解决方案，名为 D-RISA，通过该方案能够实现最优的 RIS 部署。在法国雷恩火车站的室内场景中验证了该框架，与现有方法相比，该框架展示了更好的覆盖，即最小信噪比提高 10 dB，计算时间更短（高达 25％），并提高了对更密集网络部署的可扩展性。