Joint Detection and Velocity Estimation in OFDM-ISAC Cell-Free Massive MIMO Networks

TL;DR

This paper introduces a Doppler-aware sensing framework for OFDM-based cell-free massive MIMO networks, improving target velocity estimation and detection accuracy in high-mobility scenarios.

Contribution

It develops a scalable, Doppler-aware GLRT detection method with advanced search strategies and analyzes the impact of Doppler mismatch and subcarrier diversity.

Findings

PSO-aided detector offers the best accuracy-complexity balance

Doppler mismatch significantly degrades sensing SNR in high-mobility cases

More OFDM subcarriers improve sensing SNR through frequency diversity

Abstract

This paper develops a Doppler-aware sensing framework for cell-free massive MIMO (CF-mMIMO) networks operating under OFDM-based integrated sensing and communication (ISAC). The framework explicitly incorporates the 3D-bistatic Doppler geometry across distributed access points (APs) into a generalized likelihood ratio test (GLRT) detector. To address the scalability, a user-target-centric AP association approach is utilized. The 3D tangential components of the target's velocity vector are estimated, and several search and optimization strategies, including coarse grid search, gradient-based refinement, and particle swarm optimization (PSO), are developed and evaluated. The Doppler-aware GLRT statistic and receive sensing signal-to-noise ratio (SNR) are derived. Simulation results demonstrate that the proposed PSO-aided detector achieves the most favorable accuracy-complexity trade-off, while Doppler mismatch can cause substantial sensing-SNR degradation in high-mobility scenarios. Additionally, leveraging more OFDM subcarriers enhances frequency-domain diversity and yields further sensing-SNR gains.