Fewer Surfaces, Stronger Links: EMI-Aware IRS Number, Placement, and Phase Design Under Rate Guarantees

Intelligent Reflecting Surfaces (IRSs) have emerged as a promising solution for enhancing wireless communication performance by reconfiguring the propagation environment in a passive and energy-efficient manner. However, determining the optimal number of IRSs, their spatial deployment, and the number of reflecting elements under practical constraints remains a challenging problem, particularly in the presence of electromagnetic interference (EMI). In this paper, we propose an EMI-aware optimization framework for multi-IRS-assisted wireless communication systems that jointly optimizes the number of IRSs, their placement, and their phase shift configurations while guaranteeing a minimum achievable data rate. To address the resulting high-dimensional and non-convex optimization problem, a novel Growth Optimizer (GO) algorithm is developed. The proposed approach explicitly accounts for practical deployment constraints, including spatial overlap, rate requirements, and interference effects. Extensive simulation results demonstrate that theGOalgorithm consistently outperforms benchmark optimization methods such as Differential Evolution (DE), Gradient-Based Optimization (GBO), and Particle Swarm Optimization (PSO). Specifically, GO achieves the required rate guarantees using fewer IRS units and a reduced number of reflecting elements, thereby significantly lowering system complexity while maintaining robust performance under EMI. These results highlight the effectiveness of the proposed framework for efficient and scalable IRS deployment in next-generation wireless networks.