Grid-Refinement SBL for AFDM Channel Estimation Under Fractional Delay and Fractional Doppler Channels

Abstract

This paper investigates channel estimation for affine frequency division multiplexing (AFDM) systems over channels with fractional delay and fractional Doppler (FDFD) shifts. An exact FDFD channel model is first established, and the corresponding input--output relationship is also derived. To address the FDFD-induced delay--Doppler coupling, a first-order linear approximation grid-refinement sparse Bayesian learning (FLA-GR-SBL) method is developed. To further remedy the convergence and stability issues caused by the limited accuracy of the first-order approximation, we propose a two-dimensional grid-approximation SBL (2D-GA-SBL) algorithm, which iteratively refines the delay--Doppler grid and thereby characterizes the channel more accurately. Simulation results demonstrate that the proposed algorithms significantly outperform existing on-grid and off-grid SBL benchmarks in terms of both normalized mean square error (NMSE) and bit error rate (BER).