Quasi-Static IRS: 3D Shaped Beamforming for Area Coverage Enhancement

2025-05-02

AI Summary

Intelligent Reflecting Surfaces (IRSs) suffer from high hardware cost and poor scalability due to the massive number of elements required to compensate for double-path loss. Method: This paper proposes a quasi-static IRS (QS-IRS), which achieves low-cost, low-complexity, large-scale, long-term regional coverage enhancement via mechanical or manual reconfiguration of the array topology—abandoning conventional dynamic phase control. We establish a radiation pattern model and jointly optimize mainlobe shape masking and sidelobe constraints. An alternating optimization framework is developed, integrating difference-of-convex (DC) decomposition and successive convex approximation (SCA), while incorporating 3D beam shaping and accurate element radiation pattern (ERP) modeling. Results: The proposed method significantly reduces computational complexity while achieving power gain close to the joint optimization upper bound. It effectively suppresses energy leakage and improves both coverage uniformity and energy efficiency within the target region.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements

Abstract

Intelligent reflecting surface (IRS) is a promising paradigm to reconfigure the wireless environment for enhanced communication coverage and quality. However, to compensate for the double pathloss effect, massive IRS elements are required, raising concerns on the scalability of cost and complexity. This paper introduces a new architecture of quasi-static IRS (QS-IRS), which tunes element phases via mechanical adjustment or manually re-arranging the array topology. QS-IRS relies on massive production/assembly of purely passive elements only, and thus is suitable for ultra low-cost and large-scale deployment to enhance long-term coverage. To achieve this end, an IRS-aided area coverage problem is formulated, which explicitly considers the element radiation pattern (ERP), with the newly introduced shape masks for the mainlobe, and the sidelobe constraints to reduce energy leakage. An alternating optimization (AO) algorithm based on the difference-of-convex (DC) and successive convex approximation (SCA) procedure is proposed, which achieves shaped beamforming with power gains close to that of the joint optimization algorithm, but with significantly reduced computational complexity.

Problem

• Enhancing wireless coverage using quasi-static IRS with passive elements
• Reducing cost and complexity in IRS deployment via mechanical adjustments
• Optimizing beamforming with ERP and sidelobe constraints for energy efficiency

Innovation

• Quasi-static IRS with mechanical phase tuning
• Passive elements for low-cost large-scale deployment
• DC and SCA based shaped beamforming algorithm