A Variational Bayesian Detector for Affine Frequency Division Multiplexing

TL;DR

This paper introduces a variational Bayesian detector for AFDM systems that offers low-complexity, robust, and efficient soft-decision detection, outperforming traditional methods in BER and convergence speed.

Contribution

It presents a novel VB-based detection method for AFDM that reduces complexity and enhances robustness compared to existing approaches.

Findings

Lower bit error rates than ZF, LMMSE, and MPA methods.

Faster convergence and improved robustness in complex channels.

Demonstrated computational efficiency through simulations.

Abstract

This paper proposes a variational Bayesian (VB) detector for affine frequency division multiplexing (AFDM) systems. The proposed method estimates the symbol probability distribution by minimizing the Kullback-Leibler (KL) divergence between the true posterior and an approximate distribution, thereby enabling low-complexity soft-decision detection. Compared to conventional approaches such as zero-forcing (ZF), Linear minimum mean square rrror (LMMSE), and the message passing algorithm (MPA), the proposed detector demonstrates lower bit error rates (BER), faster convergence, and improved robustness under complex multipath channels. Simulation results confirm its dual advantages in computational efficiency and detection performance.