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

Tianyu Yang, Kangda Zhi, Shuangyang Li, Giuseppe Caire

TL;DR

The paper tackles near-field imaging in wideband wireless networks by framing image recovery as a block MMV compressed sensing problem with frequency-varying sensing matrices and cross-frequency correlation captured by a frequency-correlation matrix $\\Psi$. It introduces a Sparse Bayesian Learning (SBL) approach to jointly estimate image coefficients and their frequency correlation, and enhances performance with two illumination designs: Total Coherence Minimization (TCM) and Illumination Power Maximization (IPM). The methods are validated through simulations showing superior imaging accuracy across SNR regimes and demonstrate that varied illumination patterns improve sensing matrix properties and SNR. The work advances wireless imaging in ISAC/IIAC settings by leveraging frequency correlation and tailored illumination, enabling higher-resolution near-field sensing in practical networks.

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.

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

TL;DR

The paper tackles near-field imaging in wideband wireless networks by framing image recovery as a block MMV compressed sensing problem with frequency-varying sensing matrices and cross-frequency correlation captured by a frequency-correlation matrix . It introduces a Sparse Bayesian Learning (SBL) approach to jointly estimate image coefficients and their frequency correlation, and enhances performance with two illumination designs: Total Coherence Minimization (TCM) and Illumination Power Maximization (IPM). The methods are validated through simulations showing superior imaging accuracy across SNR regimes and demonstrate that varied illumination patterns improve sensing matrix properties and SNR. The work advances wireless imaging in ISAC/IIAC settings by leveraging frequency correlation and tailored illumination, enabling higher-resolution near-field sensing in practical networks.

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.
Paper Structure (8 sections, 33 equations, 2 figures, 1 table)

This paper contains 8 sections, 33 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. System model
  3. SBL-based Wideband Imaging
  4. Illumination optimization
  5. Total Coherence Minimization (TCM)
  6. Illumination Power Maximization (IPM)
  7. Simulation Results
  8. Conclusion

Figures (2)

  • Figure 1: Considered imaging-centric near-field IIAC with one transmitter (Tx) and one receiver (Rx) equipped with ULA to recover image in the near-field region of interest (ROI).
  • Figure 2: True and estimated images at subcarrier-1 and subcarrier-4, (b-d) are under high SNR, (f-h) are under low SNR.