Fine-Grained Analysis of Reconfigurable Intelligent Surface-Assisted mmWave Networks

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for the next generation networks. By utilizing tools from stochastic geometry, we develop a meta distributed-based analytical framework to study the effect of the large-scale deployment of the RIS on the performance of a millimeter wave (mmWave) cellular network. Specifically, the locations of the base stations (BSs) are modeled as Poisson point processes (PPPs). In addition, the blockages are modeled by a Boolean model and a fraction of the blockages are coated with RISs. By considering the randomness of the locations and orientations of the RISs and the particular characteristics of mmWave communications, we provide a statistical characterization of the path loss for the BSs and RISs and derive the analytical expressions for the k-th moment of the conditional success probability, the area spectral efficiency and the energy efficiency. Numerical results demonstrate that better coverage performance and higher energy efficiency can be achieved by a large-scale deployment of RISs.