On Models with Power Conservation in Reflective Intelligent Surfaces and their Design Implications

Reconfigurable intelligent surfaces (RISs) are potential enablers of future wireless communications and sensing applications and use-cases. The RIS is envisioned as a dynamically controllable surface that is capable of transforming impinging electromagnetic waves in terms of angles and polarization. Many models has been proposed to predict the wave-transformation capabilities of potential RISs, where power conservation is ensured by enforcing that the scattered power equals the power impinging upon the aperture of the RIS, without considering whether the scattered field adds coherently of destructively with the source field. In effect, this means that power is not conserved, as elaborated in this paper. With the goal of investigating the implications of global and local power conservation in RISs, work considers a single-layer metasurface based RIS. A complete end-to-end communications channel is given through polarizability modeling and conditions for power conservation and channel reciprocity are derived. The implications of the power conservation conditions upon the end-to-end communications channel is analyzed.

Geometry-informed Channel Statistics Prediction Based upon Uncalibrated Digital Twins

Digital twins (DTs) of wireless environments can be utilized to predict the propagation channel and reduce the overhead of required to estimate the channel statistics. However, direct channel prediction requires data-intensive calibration of the DT to capture the environment properties relevant for propagation of electromagnetic signals. We introduce a framework that starts from a satellite image of the environment to produce an uncalibrated DT, which has no or imprecise information about the materials and their electromagnetic properties. The key idea is to use the uncalibrated DT to implicitly provide a geometric prior for the environment. This is utilized to inform a Gaussian process (GP), which permits the use of few channel measurements to attain an accurate prediction of the channel statistics. Additionally, the framework is able to quantify the uncertainty in channel statistics prediction and select rate in ultra-reliable low-latency communication (URLLC) that complies with statistical guarantees. The efficacy of the proposed geometry-informed GP is validated using experimental data obtained through a measurement campaign. Furthermore, the proposed prediction framework is shown to provide significant improvements compared to the benchmarks where i) direct channel statistics prediction is obtained using an uncalibrated DT and (ii) the GP predicts channel statistics using information about the location.