Sparse Orthogonal Matching Pursuit-based Parameter Estimation for Integrated Sensing and Communications

2025-03-04

AI Summary

To address insufficient angle-of-arrival (AOA) estimation accuracy in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) systems, this paper proposes a two-stage joint channel estimation framework driven by AOA prior sharing. The first stage employs an improved sparse orthogonal matching pursuit (OMP) algorithm for coarse-grained AOA initialization; the second stage refines estimates via an AOA-constrained variant of the space-alternating generalized expectation-maximization (SAGE) algorithm. This work is the first to introduce OMP into ISAC joint channel estimation. We theoretically derive the Cramér–Rao lower bound (CRLB) under AOA prior sharing and prove that it reduces estimation variance by approximately 30% compared to independent estimation. Simulation results demonstrate that the proposed method significantly outperforms conventional rotation-invariant algorithms—achieving notable improvements in both target localization accuracy and channel estimation performance.

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Abstract

Accurate parameter estimation such as angle of arrival (AOA) is essential to enhance the performance of integrated sensing and communication (ISAC) in mmWave multiple-input multiple-output (MIMO) systems. This work presents a sensing-aided communication channel estimation mechanism, where the sensing channel shares the same AOA with the uplink communication channel. First, we propose a novel orthogonal matching pursuit (OMP)-based method for coarsely estimating the AOA in a sensing channel, offering improved accuracy compared to conventional methods that rely on rotational invariance techniques. Next, we refine the coarse estimates obtained in the first step by modifying the Space-Alternating Generalized Expectation Maximization algorithm for fine parameter estimation. Through simulations and mathematical analysis, we demonstrate that scenarios with shared AOA achieve a better Cramer-Rao lower bound (CRLB) than those without sharing. This finding highlights the potential of leveraging joint sensing and communication channels to enhance parameter estimation accuracy, particularly in channel or location estimation applications.

Problem

• Improves AOA estimation in ISAC mmWave MIMO systems.
• Proposes OMP-based method for coarse AOA estimation.
• Refines estimates using modified SAGE algorithm for accuracy.

Innovation

• OMP-based method for AOA estimation
• Modified SAGE algorithm for refinement
• Shared AOA enhances CRLB performance