Near-Field Imaging by Exploiting Frequency Correlation in Wireless Communication Networks

2025-10-17

AI Summary

This paper addresses near-field imaging with uniform linear arrays (ULAs) in broadband wireless communications, formulating the multi-frequency channel as a compressive sensing problem with time-varying sensing matrices and frequency-domain correlation among image coefficients. To solve it, we propose a sparse Bayesian learning (SBL) algorithm that jointly estimates both the sparse image coefficients and their underlying frequency-domain correlation structure. Furthermore, we design two optimized illumination strategies: one minimizes the total coherence of the sensing matrix, while the other maximizes the signal-to-noise ratio (SNR) over the imaging region. Experimental results demonstrate that the proposed SBL method significantly outperforms conventional sparse reconstruction algorithms. The first illumination strategy enhances imaging resolution, whereas the second improves robustness—both maintaining high reconstruction accuracy under low-SNR conditions and with limited antenna elements.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• 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

Abstract

In this work, we address the near-field imaging under a wideband wireless communication network by exploiting both the near-field channel of a uniform linear array (ULA) and the image correlation in the frequency domain. We first formulate the image recovery as a special multiple measurement vector (MMV) compressed sensing (CS) problem, where at various frequencies the sensing matrices can be different, and the image coefficients are correlated. To solve such an MMV problem with various sensing matrices and correlated coefficients, we propose a sparse Bayesian learning (SBL)-based solution to simultaneously estimate all image coefficients and their correlation on multiple frequencies. Moreover, to enhance estimation performance, we design two illumination patterns following two different criteria. From the CS perspective, the first design minimizes the total coherence of the sensing matrix to increase the mutual orthogonality of the basis vectors. Alternatively, to improve SNR, the second design maximizes the illumination power of the imaging area. Numerical results demonstrate the effectiveness of the proposed SBL-based method and the superiority of the illumination designs.

Problem

• Addressing near-field imaging using wireless communication networks
• Solving multiple measurement vector compressed sensing with correlated coefficients
• Developing sparse Bayesian learning for image recovery and correlation estimation

Innovation

• Sparse Bayesian learning for near-field imaging
• Multiple measurement vector compressed sensing
• Optimized illumination patterns for SNR enhancement