Intelligent Reflecting Surface Networks with Multi-Order-Reflection Effect: System Modelling and Critical Bounds

TL;DR

This paper models, analyzes, and optimizes multi-order-reflection IRS networks, deriving bounds and conditions for interference-free transmission, with simulations confirming significant sum-rate improvements across various topologies.

Contribution

It introduces a comprehensive model for multi-order-reflection IRS networks and derives analytical bounds and optimal conditions for sum-rate maximization.

Findings

Sum-rate upper bounds are significantly improved with multi-piece IRS.

Optimal conditions for interference-free transmission are analytically derived.

Simulation results confirm theoretical bounds and improvements.

Abstract

In this paper, we model, analyze and optimize the multi-user and multi-order-reflection (MUMOR) intelligent reflecting surface (IRS) networks. We first derive a complete MUMOR IRS network model applicable for the arbitrary times of reflections, size and number of IRSs/reflectors. The optimal condition for achieving sum-rate upper bound with one IRS in a closed-form function and the analytical condition to achieve interference-free transmission are derived, respectively. Leveraging this optimal condition, we obtain the MUMOR sum-rate upper bound of the IRS network with different network topologies, where the linear graph (LG), complete graph (CG) and null graph (NG) topologies are considered. Simulation results verify our theories and derivations and demonstrate that the sum-rate upper bounds of different network topologies are under a K-fold improvement given K-piece IRS.