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The technology of reconfigurable intelligent surfaces (RIS) has been showingpromising potential in a variety of applications relying on Beyond-5G networks.Reconfigurable intelligent surface (RIS) can indeed provide fine channelflexibility to improve communication quality of service (QoS) or restorelocalization capabilities in challenging operating conditions, whileconventional approaches fail (e.g., due to insufficient infrastructure, severeradio obstructions). In this paper, we tackle a general low-complexity approachfor optimizing the precoders that control such reflective surfaces underhardware constraints. More specifically, it allows the approximation of anydesired beam pattern using a pre-characterized look-up table of feasiblecomplex reflection coefficients for each RIS element. The proposed method isfirst evaluated in terms of beam fidelity for several examples of RIS hardwareprototypes. Then, by means of a theoretical bounds analysis, we examine theimpact of RIS beams approximation on the performance of near-field downlinkpositioning in non-line-of-sight conditions, while considering several RISphase profiles (incl. directional, random and localization-optimal designs).Simulation results in a canonical scenario illustrate how the introduced RISprofile optimization scheme can reliably produce the desired RIS beams underrealistic hardware limitations. They also highlight its sensitivity to both theunderlying hardware characteristics and the required beam kinds in relation tothe specificity of RIS-aided localization applications.

Performance of RIS-Aided Nearfield Localization under Beams Approximation from Real Hardware Characterization