Joint Phase Noise and Off-Grid Channel Estimation for AFDM Systems via Sparse Bayesian Learning

TL;DR

This paper introduces a joint estimation method for phase noise and off-grid channels in AFDM systems using sparse Bayesian learning, improving accuracy and reducing error floors.

Contribution

It proposes a novel EM-based joint estimation approach with subspace projection and dynamic grid evolution to handle PN and off-grid errors effectively.

Findings

Significantly outperforms existing methods in NMSE and BER.

Closely approaches perfect CSI performance under practical PN conditions.

Uses reduced-rank subspace and dynamic grid strategies for efficient estimation.

Abstract

In practical affine frequency division multiplexing (AFDM) systems, the intricate coupling of oscillator phase noise (PN) and off-grid fractional shifts traps conventional estimators in a severe high-SNR error floor. To address these challenges, we propose a joint PN and channel estimation method based on sparse Bayesian learning (JPNCE-SBL). Specifically, a reduced-rank subspace projection is first introduced to capture the dominant eigen-energy of the Wiener PN process. Concurrently, a dynamic grid evolution strategy is designed to iteratively eliminate off-grid errors without requiring computationally prohibitive global grid densification. Both components are integrated into a unified Expectation-Maximization (EM) framework, where the channel and PN estimates are jointly updated at each iteration to prevent error propagation. Simulation results demonstrate that JPNCE-SBL significantly outperforms existing benchmarks in both NMSE and BER, closely approaching the perfect channel state information case under practical PN conditions.