Beam Squint Calibration With Forced-Descent Sampling for Mobility-Aware Sensing in the Near-Field Massive MIMO Systems

Abstract

The evolution toward 6G networks brings near-field propagation, wideband beam squint, and mobility-induced Doppler effects to the forefront of integrated sensing and communication (ISAC). These phenomena impose stringent requirements on estimation accuracy and algorithmic efficiency in massive MIMO systems. This paper develops a unified framework to address these challenges. First, a forced-descent (FD) sampling method is proposed to suppress noise variance and reshape spectral peaks, thereby improving the robustness of near-field range estimation while retaining linear complexity. Second, building on FD-enhanced spectral information, a mobility-aware sensing model is established, which jointly leverages beam squint and Doppler shifts to enable synchronous position-velocity estimation within a single signal frame. This unified formulation replaces the conventional two-stage paradigm of static localization followed by iterative tracking, thus enhancing accuracy and ensuring real-time feasibility. Theoretical analysis establishes the variance-reduction mechanism and derives the associated Cramér-Rao lower bounds, while complexity evaluation confirms scalability. Simulation results demonstrate sub-centimeter-level ranging accuracy and robust velocity estimation performance, validating the practical potential of FD-enhanced near-field sensing for future 6G ISAC systems.