Engineering Favorable Propagation: Near-Field IRS Deployment for Spatial Multiplexing

2026-01-12

AI Summary

This work addresses the rank deficiency of cascaded channels in intelligent reflecting surface (IRS)-assisted MIMO systems caused by strong line-of-sight links, which severely limits spatial multiplexing capability. To mitigate this far-field rank-deficiency issue at its physical root, the authors propose deploying the IRS within the near-field region of the base station, leveraging spherical wavefronts generated by sparse arrays to construct decorrelated channels. A geometry-based near-field IRS deployment criterion is established, and long-term statistical channel state information is exploited to jointly optimize IRS phase shifts and power allocation, enabling a low-complexity maximum-ratio transmission (MRT) precoding design. Closed-form expressions for favorable propagation are derived through theoretical analysis. Simulation results demonstrate that the proposed approach significantly reduces inter-user channel correlation, thereby enhancing system degrees of freedom and overall performance, substantially outperforming existing benchmark schemes.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

In intelligent reflecting surface IRS assisted multiple input multiple output MIMO systems, a strong line of sight LoS link is required to compensate for the severe cascaded path loss. However, such a link renders the effective channel highly rank deficient and fundamentally limits spatial multiplexing. To overcome this limitation, this paper leverages the large aperture of sparse arrays to harness near field spherical wavefronts, and establishes a deterministic deployment criterion that strategically positions the IRS in the near field of a base station BS. This placement exploits the spherical wavefronts of the BS IRS link to engineer decorrelated channels, thereby fundamentally overcoming the rank deficiency issue in far field cascaded channels. Based on a physical channel model for the sparse BS array and the IRS, we characterize the rank properties and inter user correlation of the cascaded BS IRS user channel. We further derive a closed form favorable propagation metric that reveals how the sparse array geometry and the IRS position can be tuned to reduce inter user channel correlation. The resulting geometry driven deployment rule provides a simple guideline for creating a favorable propagation environment with enhanced effective degrees of freedom. The favorable channel statistics induced by our deployment criterion enable a low complexity maximum ratio transmission MRT precoding scheme. This serves as the foundation for an efficient algorithm that jointly optimizes the IRS phase shifts and power allocation based solely on long term statistical channel state information CSI. Simulation results validate the effectiveness of our deployment criterion and demonstrate that our optimization framework achieves significant performance gains over benchmark schemes.

Problem

• IRS
• MIMO
• spatial multiplexing
• rank deficiency
• near-field

Innovation

• Near-field IRS
• Spatial multiplexing
• Favorable propagation
• Sparse array
• Rank deficiency