A Stochastic Geometry Framework for Performance Analysis of RIS-Assisted OFDM Cellular Networks

The reconfigurable intelligent surface (RIS) technology allows one to engineer spatial diversity in complex cellular networks. This paper provides a stochastic geometry framework for the system-level performance assessment of RIS-assisted networks. To account for the inherent randomness in the spatial deployments of base stations (BSs) and RISs, we model the RIS placements as point processes (PPs) conditioned on the associated BSs, which are modeled by a Poisson point process (PPP). We assume that the system uses the orthogonal frequency division multiplexing (OFDM) technique to exploit the multipath diversity provided by RISs. The downlink coverage probability and ergodic rate can be evaluated when RISs operate as batched powerless beamformers. The resulting analytical expressions provide a general methodology for assessing the impact of a parameterized RIS model on system performance. These RIS PPs can be adapted based on the deployment strategy. We focus on modeling the RISs as a Matérn cluster process (MCP), where each RIS cluster is a finite PPP within a ring centered on its associated BS. This model connects link-level knowledge to system-level impacts, such as overall interference and the effects of imperfect channel state information (CSI). It also evaluates key RIS deployment parameters, including batch size and RIS density. Furthermore, we analyze a variant of RIS placement in which RISs are deployed around coverage holes to demonstrate the framework’s flexibility and applicability. Numerical evaluations of the analytical expressions and Monte-Carlo simulations jointly validate the proposed analytical approach and provide valuable insights into the design of future RIS-assisted cellular networks.