Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Integrated Polarimetric Sensing and Communication with Polarization-Reconfigurable Arrays

Byunghyun Lee, Rang Liu, David J. Love, James V. Krogmeier, A. Lee Swindlehurst

TL;DR

The paper addresses joint polarimetric sensing and communication (IPSAC) using polarization-reconfigurable single-port arrays to reduce RF chain costs. It develops a unified signal model incorporating depolarization, polarization leakage, and clutter, and formulates two nonconvex optimization problems: minimizing the depolarization parameter estimation error and maximizing target SINR, each under QoS and power constraints. The authors solve these problems via alternating optimization: SDR/MM-based waveform design, majorization/minimization and LP-based polarization updates, and eigenvector approaches for user polarizations, complemented by Charnes-Cooper and Dinkelbach transforms for fractional objectives. Numerical results show substantial MSE reductions and large target-SINR gains compared with static polarization and even with dual-polarized benchmarks, illustrating that polarization reconfigurability can closely approach the performance of a larger RF-chain system while maintaining a smaller hardware footprint. Overall, the IPSAC framework demonstrates significant sensing-communication gains and practical hardware savings for next-generation ISAC deployments.

Abstract

Polarization diversity offers a cost- and space-efficient solution to enhance the performance of integrated sensing and communication systems. Polarimetric sensing exploits the signal's polarity to extract details about the target such as shape, pose, and material composition. From a communication perspective, polarization diversity can enhance the reliability and throughput of communication channels. This paper proposes an integrated polarimetric sensing and communication (IPSAC) system that jointly conducts polarimetric sensing and communications. We study the use of single-port polarization-reconfigurable antennas to adapt to channel depolarization effects, without the need for separate RF chains for each polarization. We address two core sensing tasks in IPSAC systems, target parameter estimation and target detection. For parameter estimation, we consider the problem of minimizing the mean-squared error (MSE) of the target depolarization parameter estimate, which is a critical task for various polarimetric radar applications such as rainfall forecasting, vegetation identification, and target classification. To address this nonconvex problem, we apply semi-definite relaxation (SDR) and majorization-minimization (MM) optimization techniques. Next, we consider a design that maximizes the target SINR leveraging prior knowledge of the target and clutter depolarization statistics to enhance the target detection performance. To tackle this problem, we modify the solution developed for MSE minimization subject to the same quality-of-service (QoS) constraints. Extensive simulations show that the proposed polarization reconfiguration method substantially improves the depolarization parameter MSE. Furthermore, the proposed method considerably boosts the target SINR due to polarization diversity, particularly in cluttered environments.

Integrated Polarimetric Sensing and Communication with Polarization-Reconfigurable Arrays

TL;DR

The paper addresses joint polarimetric sensing and communication (IPSAC) using polarization-reconfigurable single-port arrays to reduce RF chain costs. It develops a unified signal model incorporating depolarization, polarization leakage, and clutter, and formulates two nonconvex optimization problems: minimizing the depolarization parameter estimation error and maximizing target SINR, each under QoS and power constraints. The authors solve these problems via alternating optimization: SDR/MM-based waveform design, majorization/minimization and LP-based polarization updates, and eigenvector approaches for user polarizations, complemented by Charnes-Cooper and Dinkelbach transforms for fractional objectives. Numerical results show substantial MSE reductions and large target-SINR gains compared with static polarization and even with dual-polarized benchmarks, illustrating that polarization reconfigurability can closely approach the performance of a larger RF-chain system while maintaining a smaller hardware footprint. Overall, the IPSAC framework demonstrates significant sensing-communication gains and practical hardware savings for next-generation ISAC deployments.

Abstract

Polarization diversity offers a cost- and space-efficient solution to enhance the performance of integrated sensing and communication systems. Polarimetric sensing exploits the signal's polarity to extract details about the target such as shape, pose, and material composition. From a communication perspective, polarization diversity can enhance the reliability and throughput of communication channels. This paper proposes an integrated polarimetric sensing and communication (IPSAC) system that jointly conducts polarimetric sensing and communications. We study the use of single-port polarization-reconfigurable antennas to adapt to channel depolarization effects, without the need for separate RF chains for each polarization. We address two core sensing tasks in IPSAC systems, target parameter estimation and target detection. For parameter estimation, we consider the problem of minimizing the mean-squared error (MSE) of the target depolarization parameter estimate, which is a critical task for various polarimetric radar applications such as rainfall forecasting, vegetation identification, and target classification. To address this nonconvex problem, we apply semi-definite relaxation (SDR) and majorization-minimization (MM) optimization techniques. Next, we consider a design that maximizes the target SINR leveraging prior knowledge of the target and clutter depolarization statistics to enhance the target detection performance. To tackle this problem, we modify the solution developed for MSE minimization subject to the same quality-of-service (QoS) constraints. Extensive simulations show that the proposed polarization reconfiguration method substantially improves the depolarization parameter MSE. Furthermore, the proposed method considerably boosts the target SINR due to polarization diversity, particularly in cluttered environments.

Paper Structure

This paper contains 32 sections, 3 theorems, 69 equations, 11 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Polarization-Reconfigurable Array Model
  4. Sensing Signal Model
  5. Depolarization parameter
  6. Target
  7. Communication Signal Model
  8. Depolarization MSE Minimization
  9. Problem Formulation
  10. Waveform Optimization Algorithm
  11. Polarization Optimization Algorithm
  12. Update of $\textbf{p}_t$
  13. Update of $\textbf{p}_r$
  14. Update of $\textbf{p}_{u,k}$
  15. Summary
  16. ...and 17 more sections

Key Result

Lemma 1

Let $\bar{\textbf{P}}_{i}$ be $\bar{\textbf{P}}$ at iteration $i$. The objective can be majorized as where and $\lambda_{\bm{\Omega}}$ is the maximum eigenvalue of matrix $\bm{\Omega}$.

Figures (11)

  • Figure 1: system with polarization-reconfigurable arrays. Each antenna can adjust the linear polarizations of the transmitted and received signals. The transmitted wave experiences depolarization at the target, and the backscattered signal is received at the BS receiver.
  • Figure 2: Normalized versus user threshold (Solid lines: $K=2$, dashed lines: $K=4$).
  • Figure 3: Normalized versus antenna $\chi_{ant}$ ($K=2$, $\gamma_{th}=10dB$).
  • Figure 4: Normalized versus transmit SNR ($K=2$, $\gamma_{th}=10$dB).
  • Figure 5: Normalized versus number of transmit/receive antennas ($K=2$, $\gamma_{th}=10dB$).
  • ...and 6 more figures

Theorems & Definitions (3)

  • Lemma 1
  • Lemma 2
  • Lemma 3