Optimum Reconfigurable Intelligent Surface Selection for Indoor and Outdoor Communications

The reconfigurable intelligent surface (RIS) is a promising technology that is anticipated to enable high spectrum and energy efficiencies in future wireless communication networks. This paper investigates optimum location-based RIS selection policies in RIS-aided wireless networks to maximize the signal-to noise ratio (SNR) for a power path-loss model in outdoor communications and an exponential path-loss model in indoor communications. The random locations of all available RISs are modeled as a Poisson point process (PPP). To quantify the network performance, the outage probabilities and average rates attained by the proposed RIS selection policies are evaluated by deriving the distance distribution of the chosen RIS node as per the selection policies for both power and exponential path-loss models. Feedback could incur heavy signaling overhead. To reduce the overhead, we also propose limited-feedback RIS selection policies by limiting the average number of RISs that feed back their location information to the source. The outage probabilities and average rates obtained by the limited-feedback RIS selection policies are derived for both path-loss models. The numerical results show notable performance gains obtained by the proposed RIS selection policies and demonstrate that the conventional relay selection policies are not suitable for RIS-aided wireless networks.