RIS-Assisted Millimeter Wave Communications for Indoor Scenarios: Modeling and Coverage Analysis
Zhi Chai, Jiajie Xu, Justin P. Coon, Mohamed-Slim Alouini
TL;DR
This work addresses accurate performance evaluation for RIS-assisted indoor mmWave networks by introducing a 3D height-aware stochastic geometry model that captures indoor tallness variations, boundary effects, blockage, and clustered user distributions. It derives distance distributions and LOS probabilities for both direct and RIS-assisted links, and provides CP expressions that couple LOS events with distance statistics. The analytical results are validated against Monte Carlo simulations, revealing how transmitter density, obstacle density, RIS density, and cluster radius shape coverage, and offering deployment guidelines on when RIS deployment yields substantial gains. The findings emphasize that obstacle density and geometry critically influence CP, while RIS benefits are most significant when transmitters are limited or power-constrained, guiding practical RIS integration in indoor facilities.
Abstract
Millimeter wave (mmWave) communications and reconfigurable intelligent surfaces (RIS) are two critical technologies for next-generation networks, especially in dense indoor environments. However, existing analyses often oversimplify the indoor environment by neglecting some of the key characteristics, such as height variations, boundary effects, blockage effects, and user spatial distributions. In this paper, we develop an improved stochastic geometry-based model for RIS-assisted mmWave communications in indoor scenarios like conference centers, hospitals, and shopping malls. The proposed model incorporates the height factor for all the nodes in the network (e.g., transmitters, users, RISs, and obstacles) and captures the user clustering behavior in these scenarios. In addition, the boundary effect is also being considered for line-of-sight (LOS) probability calculation. Analytical expressions for distance distributions, LOS probabilities, and the coverage probability (CP) are derived. The CP is then validated through Monte Carlo simulations. Our results reveal deployment insights by approximating and simplifying the derived CP expressions, showing how transmitter density, obstacle density, RIS density, and user cluster radius impact network coverage. Notably, we show that RISs significantly improve coverage when transmitters or transmit power are limited but offer marginal benefits when transmitter density is high. These findings provide practical guidelines for the design and deployment of RIS-assisted indoor mmWave networks.