Neural Equalisers for Highly Compressed Faster-than-Nyquist Signalling: Design, Performance, Complexity and Robustness

2026-05-02

AI Summary

This work addresses the severe intersymbol interference (ISI) induced by ultra-dense Faster-than-Nyquist (FTN) signaling—compressed by up to 75%—by proposing a low-complexity, low-latency deep learning-based receiver architecture. The approach integrates a sliding-window detection mechanism with a neural equalizer to effectively exploit temporal context for efficient signal equalization and symbol detection. Through a robust training strategy designed to withstand non-ideal channel conditions, the proposed receiver demonstrates strong generalization and reliability in highly compressed FTN scenarios. Notably, it provides the first experimental validation of the practical feasibility of real-time deployment for FTN systems operating at extremely low packing factors.

Abstract

Faster-than-Nyquist (FTN) signalling has emerged as a compelling technique for enhancing spectral efficiency in bandwidth-constrained communication systems. By intentionally introducing controlled intersymbol interference (ISI), FTN allows transmission at rates exceeding the traditional Nyquist limit, unlocking new potential in high-speed data communication. However, its practical deployment remains challenged by the need for low-complexity detection strategies that can cope with the induced ISI while maintaining low latency and robust performance. We propose deep learning receivers that are resilient to non-idealities. In this paper, we present a deep learning-based framework for FTN signalling that addresses these challenges through several novel contributions. First, we propose a sliding window detection method that leverages temporal context while preserving computational efficiency. Second, we demonstrate the viability of FTN systems with very low packing factors, showing that reliable performance can be achieved even under aggressive spectral compression (up to 75\%). Our architecture is optimised for low latency and low complexity, making it suitable for real-time applications and scalable deployment. In addition, we assess the robustness of our models across varying channel conditions and noise profiles, providing insights into their generalisability and resilience.

Problem

• Faster-than-Nyquist signalling
• intersymbol interference
• low-complexity detection
• spectral efficiency
• robustness

Innovation

• Faster-than-Nyquist signalling
• neural equaliser
• sliding window detection
• spectral compression
• deep learning receiver