Near-Field Time-Varying Channel: Analysis and Tracking

In 6G and future communication systems, the adoption of large scale antenna arrays and high frequency bands is an inevitable trend. This leads to most users operating in the near-field region, where higher angular resolution and sensitivity to distance changes make user mobility effects fundamentally different from those in the far-field. In this work, a systematic study of near-field channel dynamics under user mobility is investigated. We begin by analyzing the Doppler characteristics associated with both line of sight (LoS) and multipath components, and demonstrate that notable Doppler shifts can occur even at low user velocities. In particular, the LoS component exhibits antenna dependent Doppler shifts, highlighting the spatially non-uniform nature of Doppler effects in near-field scenarios. Based on this, we derive a closed form expression for the coherence time in scattering-free environments, which reveals its dependence on antenna aperture, wavelength, user motion, etc. Unlike far-field LoS channels where Doppler effects can be compensated by a phase rotation, the near-field counterpart exhibits antenna dependent Doppler shifts, resulting in a finite coherence time even without scattering. For multipath scenarios, where the near-field channel is inherently sparse, we establish upper and lower bounds for the temporal correlation function. Furthermore, to track time-varying channels, we propose an array segmentation strategy and formulate a state-space model of the near-field channel in the dictionary domain. The proposed estimation framework comprises an expectation maximization (EM) stage using Kalman filtering with Rauch-Tung-Striebel smoothing (KF-RTSS) for virtual channel statistics and a fixed-point iteration for parameter refinement, followed by a Kalman filtering stage for channel prediction. A detailed complexity analysis is also provided. Finally, extensive simulations validate our theoretical analysis and show that the proposed method accurately captures near-field channel dynamics, offering robust performance across different angles, distances, and signal to noise ratios.