6 research outputs found
Performance of RIS-Aided Nearfield Localization under Beams Approximation from Real Hardware Characterization
The technology of reconfigurable intelligent surfaces (RIS) has been showing
promising potential in a variety of applications relying on Beyond-5G networks.
Reconfigurable intelligent surface (RIS) can indeed provide fine channel
flexibility to improve communication quality of service (QoS) or restore
localization capabilities in challenging operating conditions, while
conventional approaches fail (e.g., due to insufficient infrastructure, severe
radio obstructions). In this paper, we tackle a general low-complexity approach
for optimizing the precoders that control such reflective surfaces under
hardware constraints. More specifically, it allows the approximation of any
desired beam pattern using a pre-characterized look-up table of feasible
complex reflection coefficients for each RIS element. The proposed method is
first evaluated in terms of beam fidelity for several examples of RIS hardware
prototypes. Then, by means of a theoretical bounds analysis, we examine the
impact of RIS beams approximation on the performance of near-field downlink
positioning in non-line-of-sight conditions, while considering several RIS
phase profiles (incl. directional, random and localization-optimal designs).
Simulation results in a canonical scenario illustrate how the introduced RIS
profile optimization scheme can reliably produce the desired RIS beams under
realistic hardware limitations. They also highlight its sensitivity to both the
underlying hardware characteristics and the required beam kinds in relation to
the specificity of RIS-aided localization applications.Comment: 27 pages, 8 figures, journa