  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
end-to-end signal-to-noise ratio for product-scaling and sum-scaling path-loss
models where the received power scales with the product and sum of the
transmitter-to-RIS and RIS-to-receiver distances, respectively. These scaling
laws cover the important cases of end-to-end path-loss models in RIS-aided
wireless systems. The random locations of all available RISs are modeled as a
Poisson point process. 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 product-scaling and sum-scaling path-loss
models. We also propose a limited-feedback RIS selection framework to achieve
distributed network operation. The outage probabilities and average rates
obtained by the limited-feedback RIS selection policies are derived for both
path-loss models as well. The numerical results show notable performance gains
obtained by the proposed RIS selection policies.
