The integration of Smart Surfaces in 6G communication networks, also dubbed as Reconfigurable Intelligent Surfaces (RISs), is a promising paradigm change gaining significant attention given its disruptive features. RISs are a key enabler in the realm of 6G Integrated Sensing and Communication (ISAC) systems where novel services can be offered together with the future mobile networks communication capabilities. This paper addresses the critical challenge of precisely localizing users within a communication network by leveraging the controlled-reflective properties of RIS elements without relying on more power-hungry traditional methods, e.g., GPS, adverting the need of deploying additional infrastructure and even avoiding interfering with communication efforts. Moreover, we go one step beyond: we build COLoRIS, anOpportunistic ISACapproach that leverages localization-agnostic RIS configurations to accurately position mobile users via trained learning models. Extensive experimental validation and simulations in large-scale synthetic scenarios show$\mathbf{5\%}$positioning errors (with respect to field size) under different conditions. Further, we show that a low-complexity version running in a limited off-the-shelf (embedded, low-power) system achieves positioning errors in the$\mathbf{11\%}$range at a negligible$\mathbf{+2.7\%}$energy expense with respect to the classical RIS.

The advance towards 6G networks comes with the promise of unprecedented performance in sensing and communication capabilities. The feat of achieving those, while satisfying the ever-growing demands placed on wireless networks, promises revolutionary advancements in sensing and communication technologies. As 6G aims to cater to the growing demands of wireless network users, the implementation of intelligent and efficient solutions becomes essential. In particular, reconfigurable intelligent surfaces (RISs), also known as Smart Surfaces, are envisioned as a transformative technology for future 6G networks. The performance of RISs when used to augment existing devices is nevertheless largely affected by their precise location. Suboptimal deployments are also costly to correct, negating their low-cost benefits. This paper investigates the topic of optimal RISs diffusion, taking into account the improvement they provide both for the sensing and communication capabilities of the infrastructure while working with other antennas and sensors. We develop a combined metric that takes into account the properties and location of the individual devices to compute the performance of the entire infrastructure. We then use it as a foundation to build a reinforcement learning architecture that solves the RIS deployment problem. Since our metric measures the surface where given localization thresholds are achieved and the communication coverage of the area of interest, the novel framework we provide is able to seamlessly balance sensing and communication, showing its performance gain against reference solutions, where it achieves simultaneously almost the reference performance for communication and the reference performance for localization.

As upcoming, beyond-5G (B5G) wireless network generations are expected to deliver much better performance than existing solutions, Reconfigurable intelligent surfaces (RISs) are gaining relevance as one of the new key technologies able to facilitate such improvement. Interestingly, they can redesign how the propagation environment is conceived by giving an opportunity to programmatically alter it: they can be configured to behave as orientable mirrors, scatterers, or lenses. This flexibility allows for the successful exploitation of bands which provide superior performance in wireless links but present poor propagation properties. However, this fascinating technology comes at not negligible costs: RISs require ad-hoc design, deployment and management operations to be fully exploited. In this paper, we tackle one of the open problems in the RISs literature: the optimal placement. We propose a model-based and a model-free approach, respectively RISA and AI-RISA, showcasing their large-scale solutions on synthetic topologies to improve communication performance while solving the “dead-zone” coverage problem. Additionally, our frameworks are empirically validated within a realistic indoor scenario, the Rennes railway station, showing how a complex indoor propagation environment can be fully disciplined by an advanced RISs installation.

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, obtains an 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 %) while improving scalability towards denser network deployments.

Future generations of wireless networks will offer unrivalled performance via unprecedented solutions: meta-surfaces will drive such revolution by enabling control over the surrounding propagation environment, always portrayed as a tamper-proof black box. The reconfigurable intelligent surface (RIS) technology, envisioned as the discrete version of a metasurface, can dynamically alter the propagation of the impinging signals by, e.g., steering the corresponding beams towards controllable directions. This will unlock new application opportunities and deliver advanced end-user services.However, this fascinating solution comes at non-negligible costs: RISs require ad-hoc design, deployment and management operations to be fully exploited. In this paper, we tackle the RISs placement problem from a theoretical viewpoint, showcasing a large-scale solution on synthetic topologies to improve communication performance while solving the dead-zone problem. Additionally, our mathematical framework is empirically validated in a realistic indoor scenario, the Rennes railway station, showing how a complex indoor propagation environment can be fully disciplined by an advanced RIS installation.