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arXiv · 2604.18056

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

Maryam Darabi·Sergi Liesegang·Emanuele Grossi·Stefano Buzzi
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Abstract

This paper develops a sensing framework for cell-free massive MIMO (CF-mMIMO) networks operating under orthogonal frequency division multiplexing (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 system scalability, a user-target-centric AP association approach is utilized. The 3D velocity vector of the target is estimated, and several search and optimization strategies, including coarse grid search, gradient-based refinement, and particle swarm optimization (PSO), are developed and evaluated. Simulation results demonstrate that the proposed PSO-aided detector achieves the most favorable accuracy-complexity trade-off, while Doppler mismatch can cause substantial sensing signal-to-noise ratio (SNR) degradation in high-mobility scenarios. Additionally, leveraging more OFDM subcarriers enhances frequency-domain diversity and yields further sensing-SNR gains.

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Maryam Darabi, Sergi Liesegang, Emanuele Grossi, Stefano Buzzi. 2026-04-20. Joint Detection and Velocity Estimation in OFDM-ISAC Cell-Free Massive MIMO Networks. https://arxiv.org/abs/2604.18056

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