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.

翻译：可重构智能表面（RIS）的出现为即将到来的超五代网络（B5G）无线技术带来了显著改进。RIS在影响传播环境方面展现出的灵活性，为网络设计者提供了可编程方式改变无线信道的可能性，从而利用更高频段实现卓越吞吐量，同时克服这些频段中极具挑战性的电磁传播特性。然而，RIS并非万能灵药。其应用伴随着巨大复杂性，需要特定的部署和管理操作才能见效。本文针对将RIS投入实际应用这一开放性问题展开研究，重点关注覆盖薄弱或缺失区域。我们首次提出一种名为D-RISA的深度强化学习（DRL）解决方案，该方案通过训练DRL智能体进而获得最优RIS部署。我们在法国雷恩火车站室内场景验证了该框架，并将算法性能与现有最优方法（SOA）进行对比评估。基准测试表明，本方案在降低计算时间（最高达-25%）的同时实现了更优覆盖（最小信噪比提升10分贝），且对更密集网络部署具有更好的可扩展性。