Low Complexity Classification Approach for Faster-than-Nyquist (FTN) Signalling Detection

TL;DR

This paper introduces a low-complexity machine learning-based classifier for faster-than-Nyquist signaling detection, significantly reducing computational complexity while maintaining detection performance.

Contribution

It proposes a novel low-complexity classifier that exploits FTN ISI structure, reducing the detection problem to a lower-dimensional space with offline and online stages.

Findings

Effective reduction in detection complexity

Maintains high detection accuracy

Supports soft-output detection

Abstract

Faster-than-Nyquist (FTN) signaling can improve the spectral efficiency (SE); however, at the expense of high computational complexity to remove the introduced intersymbol interference (ISI). Motivated by the recent success of ML in physical layer (PHY) problems, in this paper we investigate the use of ML in reducing the detection complexity of FTN signaling. In particular, we view the FTN signaling detection problem as a classification task, where the received signal is considered as an unlabeled class sample that belongs to a set of all possible classes samples. If we use an off-shelf classifier, then the set of all possible classes samples belongs to an $N$-dimensional space, where $N$ is the transmission block length, which has a huge computational complexity. We propose a low-complexity classifier (LCC) that exploits the ISI structure of FTN signaling to perform the classification task in $N_p \ll N$-dimension space. The proposed LCC consists of two stages: 1) offline pre-classification that constructs the labeled classes samples in the $N_p$-dimensional space and 2) online classification where the detection of the received samples occurs. The proposed LCC is extended to produce soft-outputs as well. Simulation results show the effectiveness of the proposed LCC in balancing performance and complexity.