A Novel Sum-Product Detection Algorithm for Faster-than-Nyquist Signaling: A Deep Learning Approach

TL;DR

This paper introduces a deep learning assisted sum-product detection algorithm for faster-than-Nyquist signaling, enhancing detection accuracy by integrating neural networks into the factor graph to approach MAP performance with manageable complexity.

Contribution

It proposes a novel neural network-based modification to the sum-product algorithm for FTN detection, improving performance and enabling Turbo equalization integration.

Findings

Approaches MAP error performance in FTN detection.

Improves BER performance in finite-length coded FTN systems.

Compatible with Turbo equalization for enhanced detection.

Abstract

A deep learning assisted sum-product detection algorithm (DL-SPDA) for faster-than-Nyquist (FTN) signaling is proposed in this paper. The proposed detection algorithm works on a modified factor graph which concatenates a neural network function node to the variable nodes of the conventional FTN factor graph to approach the maximum a posterior probabilities (MAP) error performance. In specific, the neural network performs as a function node in the modified factor graph to deal with the residual intersymbol interference (ISI) that is not considered by the conventional detector with a limited complexity. We modify the updating rule in the conventional sum-product algorithm so that the neural network assisted detector can be complemented to a Turbo equalization receiver. Furthermore, we propose a compatible training technique to improve the detection performance of the proposed DL-SPDA with Turbo equalization. In particular, the neural network is optimized in terms of the mutual information between the transmitted sequence and the extrinsic information. We also investigate the maximum-likelihood bit error rate (BER) performance of a finite length coded FTN system. Simulation results show that the error performance of the proposed algorithm approaches the MAP performance, which is consistent with the analytical BER.