Machine-Vision Aided Low-Complexity Beamforming Methods for IRS Networks

Abstract

This paper uses machine vision (MV) to assist communication in wireless networks, with a specific focus on intelligent reflecting surface (IRS)-assisted wireless systems. Instead of relying on traditional schemes such as alternating optimization or semidefinite relaxation, which are computationally expensive and often impractical, we use visual data to enable low-complexity beamforming decisions for users. Our approach employs a ceiling-mounted camera with a fish-eye lens to cover a wide communication area. Users within the network are first detected using an object detection method. We then propose closed-form analytical expressions to determine the distances between the access point (AP), users, and IRS. To address the non-uniform distortion inherent in fish-eye images, we introduce a novel method for determining non-uniform pixel weightings using trigonometric techniques. Based on the calculated distances, beamforming decisions are made according to the user’s proximity to the AP or IRS. Furthermore, we extend the application of MV to a multiuser IRS-assisted scenario, where we propose grouping users into either two or three categories. Using these visually identified groups, we simplify and solve the IRS optimization problem by considering only the constraints relevant to each category. Simulation results demonstrate that the proposed MV-based scheme for IRS achieves a significantly lower computational cost compared to benchmark schemes, while maintaining comparable performance.