mmWave Coverage Extension Using Reconfigurable Intelligent Surfaces in Indoor Dense Spaces

TL;DR

This paper explores deploying reconfigurable intelligent surfaces to enhance millimeter-wave network coverage in indoor dense environments, using realistic simulations and optimization algorithms to strategically position RISs for improved data rates.

Contribution

It introduces an integrated simulation and optimization framework for RIS deployment in indoor mmWave networks, demonstrating effective coverage extension and rate guarantees.

Findings

At least four RISs significantly extend coverage.

Achieves 130 Mbit/s data rate for UEs in airplane cabin.

Proposes a joint RIS placement and phase-shift optimization algorithm.

Abstract

In this work, we consider the deployment of reconfigurable intelligent surfaces (RISs) to extend the coverage of a millimeter-wave (mmWave) network in indoor dense spaces. We first integrate RIS into ray-tracing simulations to realistically capture the propagation characteristics, then formulate a non-convex optimization problem that minimizes the number of RISs under rate constraints. We propose a feasible point pursuit and successive convex approximation-based algorithm, which solves the problem by jointly selecting the RIS locations, optimizing the RIS phase-shifts, and allocating time resources to user equipments (UEs). The numerical results demonstrate substantial coverage extension by using at least four RISs, and a data rate of 130 Mbit/s is guaranteed for UEs in the considered area of an airplane cabin.