Low-Complexity Soft-Feedback Detector for AFDM Systems

TL;DR

This paper introduces a low-complexity soft-feedback detection scheme for AFDM systems that improves accuracy and outperforms existing detectors, especially at low SNR levels.

Contribution

A novel soft-feedback detector based on MRC framework that mitigates error propagation and enhances detection accuracy in AFDM systems.

Findings

Achieves approximately 3 dB SNR gain at BER of 10^{-3}

Outperforms benchmark decision-feedback detectors

Maintains low computational complexity

Abstract

Affine frequency division multiplexing (AFDM), an emerging multi-carrier modulation scheme, has garnered significant attention due to its resilience to Doppler shifts and capability to achieve full diversity in doubly dispersive channels. However, existing data detection algorithms for AFDM systems face a significant trade-off between computational complexity and accuracy. In this paper, a novel low-complexity data detection scheme, termed the soft-feedback detector (SFD), is proposed. Particularly, building upon a maximum ratio combining (MRC) estimator framework, the SFD leverages the a priori symbol distribution to mitigate error propagation during iterative detection. Specifically, soft-decision feedback is incorporated as extrinsic information derived from the log-likelihood ratios of the transmitted symbols. As a result, the proposed detector significantly enhances detection accuracy while maintaining low computational complexity. Simulation results demonstrate that the SFD consistently outperforms benchmark decision-feedback detectors. In particular, compared with the conventional MRC detector, the proposed scheme achieves approximately a 3 dB signal-to-noise ratio (SNR) gain at the bit error rate (BER) of $10^{-3}$.