Reconfigurable intelligent surface (RIS) enables the control of wireless channels to improve coverage. To further extend coverage, multi-RIS aided systems have been explored, where multiple RISs steer the signal via a multi-hop path. However, deriving a physics-compliant channel model for multi-RIS aided systems is still an open problem. In this study, we fill this gap by modeling multi-RIS aided systems through multiport network theory, and deriving a channel model accounting for impedance mismatch, mutual coupling, and structural scattering. The derived physics-compliant model differs from the model widely used in literature, which omits the RIS structural scattering. To quantify this difference, we derive the channel gain scaling laws of the two models under line-of-sight (LoS) and multipath channels. Theoretical insights, validated by numerical results, show an important discrepancy between the physics-compliant and the widely used models, increasing with the number of RISs and multipath richness. In a multi-hop system aided by four 128-element RISs with multipath channels, optimizing the RISs using the widely used model and applying their solutions to the physics-compliant model achieves only 7% of the maximum channel gain. This highlights how severely mismatched channel models can be, calling for more accurate models in communication theory.

Accurate signal localization is critical for Internet of Things applications, but precise propagation models are often unavailable due to uncontrollable factors. Simplified models, such as planar and spherical wavefront approximations, are widely used but can cause model mismatches that reduce accuracy. To address this, we propose an expected likelihood framework for model mismatch analysis and online (on-the-fly) model selection. The expected likelihood idea is that under the Gaussian assumption of independent samples, the likelihood ratio of the actual covariance matrix of the received signal is described by a distribution that depends only on the number of samples and receive antennas, not the true covariance itself. This scenario independence allows us to avoid the true model need-to-know requirement for model mismatch analysis. In this paper, we formulate an expected likelihood approach for online model and estimation parameters selection, and demonstrate its applicability and efficiency using the analytical electromagnetic model for data generation and considering different simplified models for source localization in both direct and reconfigurable intelligent surface assisted diverse IoT environments.

This paper presents a theoretical and mathematical framework for the design of a conformal reconfigurable intelligent surface (RIS) that adapts to non-planar geometries, which is a critical advancement for the deployment of RIS on non-planar and irregular surfaces as envisioned in smart radio environments. Previous research focused mainly on the optimization of RISs assuming a predetermined shape, while neglecting the intricate interplay between shape optimization, phase optimization, and mutual coupling effects. Our contribution, the Tailored 3D RIS (T3DRIS) framework, addresses this fundamental problem by integrating the configuration and shape optimization of RISs into a unified model and design framework, thus facilitating the application of RIS technology to a wider spectrum of environmental objects. The mathematical core of T3DRIS is rooted in optimizing the 3D deployment of the unit cells and tuning circuits, aiming at maximizing the communication performance. Through rigorous full-wave simulations and a comprehensive set of numerical analyses, we validate the proposed approach and demonstrate its superior performance and applicability over contemporary designs. This study—the first of its kind—paves the way for a new direction in RIS research, emphasizing the importance of a theoretical and mathematical perspective in tackling the challenges of conformal RISs.

We describe a mathematical physics approach to electromagnetic information theory. The approach is based on quantum tunnelling theories that are formulated in a ray dynamical phase-space. Starting from a scattering model of waves transmitted across two holographic surfaces, we show that the eigenvalues corresponding to communication modes supported by the surfaces can be approximated as tunnelling rates. The related phase-space calculations provide simplified formulas of degrees of freedom for diversity in the near-field. The approach results in rigorous, physics-based framework for general characterisation of surface communications in free-space and confined environments.

The widespread adoption of Reconfigurable Intelligent Surfaces (RISs) in future practical wireless systems is critically dependent on the integration of the RIS into higher-layer protocols beyond the physical (PHY) one, an issue that has received minimal attention in the research literature. In light of this, we consider a classical random access (RA) problem, where uncoordinated users’ equipment (UEs) transmit sporadically to an access point (AP). Differently from previous works, we ponder how a RIS can be integrated into the design of new medium access control (MAC) layer protocols to solve such a problem. We consider that the AP is able to control a RIS to change how its reflective elements are configured, namely, the RIS configurations. Thus, the RIS can be opportunistically controlled to favor the transmission of some of the UEs without the need to explicitly perform channel estimation (CHEST). We embrace this observation and propose a RIS-assisted RA protocol comprised of two modules: Channel Oracle and Access. During channel oracle, the UEs learn how the RIS configurations affect their channel conditions. During the access, the UEs tailor their access policies using the channel oracle knowledge. Our proposed RIS-assisted protocol is able to increase the expected throughput by approximately 60% in comparison to the slotted ALOHA (S-ALOHA) protocol.

This paper is concerned with the enabling of massive connectivity in a multi-cluster network. In order to tackle significant amount of interference imposed by inter- and intra-cluster users, we consider a frequency-hopping (FH) system where an improved quasi-orthogonal hopping pattern, called optimal strong no-hit-zone FH sequence (SNHZ-FHS) set, is adopted in each cluster. We propose a flexible construction of SNHZ-FHS sets and derive their Hamming correlation values at different access delays. Since the maximum number of supportable users under traditional FH networks is strictly limited by the number of frequency slots, we investigate the design of a novel class of adaptive array beam-forming (BF) receiver in which SNHZ-FHS patterns (i.e., SNHZ-FH/BF) are reused over asynchronous multi-cluster uplink channels. Extensive numerical comparisons show that our proposed SNHZ-FH/BF system provides an effective means for attaining higher user capacity as well as remarkable capability of interference suppression.