A Variational Bayesian Detector for Affine Frequency Division Multiplexing

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.