Multi-IRS Deployment Optimization for Enhanced Wireless Coverage: A Performance-Cost Trade-off

Intelligent reflecting surface (IRS) can be densely deployed in complex environment to create cascaded line-of-sight (LoS) paths between the base station (BS) and multiple users via tunable single/multiple signal reflections, thereby significantly enhancing the BS's coverage performance. To achieve this goal, we present an optimization framework for multi-IRS deployment in this paper and study its efficient design. In particular, we assume that a set of candidate locations for deploying IRSs are given in an area of interest, and show that there exists a fundamental trade-off between maximizing the BS's coverage in the area and minimizing the total cost in multi-IRS deployment design. Specifically, the more IRSs deployed over those candidate locations, the smaller number of IRS reflections on average required for achieving a LoS link between the BS and any user location in the area, which helps reduce the cascaded path loss and thus enhance the communication performance. To optimally characterize this trade-off, we formulate the multi-IRS deployment problem based on graph theory and propose a new successive removal algorithm to efficiently solve this problem by iteratively removing IRSs from the candidate locations while satisfying a given communication performance constraint. Simulation results are provided to show the efficacy of the proposed design approach and algorithm for multi-IRS deployment.