Message Passing based Parameter Estimation in Cooperative MIMO-OFDM ISAC Systems

TL;DR

This paper presents a message-passing framework for collaborative MIMO-OFDM ISAC systems that jointly estimates target parameters, reducing complexity and communication overhead while enhancing sensing performance through multi-BS cooperation.

Contribution

It introduces a novel MP-based parameter estimation method with hierarchical Gaussian approximation for cooperative MIMO-OFDM ISAC systems, improving efficiency and accuracy.

Findings

Effective joint estimation of target position and velocity.

Significant reduction in computational complexity.

Lower inter-BS communication overhead.

Abstract

In integrated sensing and communication (ISAC) networks, multiple base stations (BSs) collaboratively sense a common target, leveraging diversity from multiple observation perspectives and joint signal processing to enhance sensing performance. This paper introduces a novel message-passing (MP)-based parameter estimation framework for collaborative MIMO-OFDM ISAC systems, which jointly estimates the target's position and velocity. First, a signal propagation model is established based on geometric relationships, and a factor graph is constructed to represent the unknown parameters. The sum-product algorithm (SPA) is then applied to this factor graph to jointly estimate the multi-dimensional parameter vector. To reduce communication overhead and computational complexity, we employ a hierarchical message-passing scheme with Gaussian approximation. By adopting parameterized message distributions and layered processing, the proposed method significantly reduces both computational complexity and inter-BS communication overhead. Simulation results demonstrate the effectiveness of the proposed MP-based parameter estimation algorithm and highlight the benefits of multi-perspective observations and joint signal processing for cooperative sensing in MIMO-OFDM ISAC systems.