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

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.

💡 Research Summary

The paper addresses the challenge of jointly estimating the position and velocity of a moving target in a cooperative MIMO‑OFDM integrated sensing and communication (ISAC) network composed of multiple base stations (BSs). Each BS simultaneously serves a single‑antenna user and performs radar sensing by transmitting OFDM waveforms. The authors first derive a geometric signal‑propagation model that relates the target’s state (x₀, y₀, vₓ, v_y) to observable quantities at each BS: round‑trip delay τₗ, azimuth angle θₗ, and Doppler shift νₗ. The radar cross‑section (RCS) αₗ of the target is modeled as a complex Gaussian random variable, capturing random amplitude and phase fluctuations across observation angles. By stacking the received OFDM symbols across sub‑carriers, antennas, and symbols, the authors obtain a compact matrix representation Yₗ for the l‑th BS, which explicitly contains the unknown target state, the per‑BS RCS, the beamforming vectors, and the Doppler‑induced phase rotation.

The estimation problem is formulated as a maximum‑a‑posteriori (MAP) inference: jointly estimate the target state vector ξ =