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.

翻译：本文针对基于OFDM集成感知与通信（ISAC）的无蜂窝大规模MIMO（CF-mMIMO）网络，提出了一种多普勒感知框架。该框架将跨分布式接入点（AP）的三维多普勒双基地几何特性显式地融入广义似然比检验（GLRT）检测器中。为解决可扩展性问题，采用了一种用户-目标为中心的AP关联方法。通过对目标速度矢量的三维切向分量进行估计，开发并评估了多种搜索与优化策略，包括粗网格搜索、基于梯度的精炼以及粒子群优化（PSO）。推导了多普勒感知GLRT统计量及接收感知信噪比（SNR）。仿真结果表明，所提出的PSO辅助检测器实现了最优的精度-复杂度权衡，而多普勒失配在高机动场景中可能导致感知信噪比的显著下降。此外，利用更多OFDM子载波可增强频域分集，并进一步带来感知信噪比增益。