Faster-Than-Nyquist Equalization with Convolutional Neural Networks

TL;DR

This paper introduces a convolutional neural network-based equalizer for Faster-than-Nyquist signaling, significantly improving spectral efficiency and error rates over traditional methods in wireless communication systems.

Contribution

It proposes a novel deep learning architecture for ISI equalization in FTN signaling, outperforming existing benchmarks and achieving higher throughput with lower error floors.

Findings

Outperforms benchmark equalizers in error rates.

Achieves 2.5 Mbps throughput at 10dB SNR with 60% compression.

Demonstrates robustness against strong inter-symbol interference.

Abstract

Faster-than-Nyquist (FTN) signaling aims at improving the spectral efficiency of wireless communication systems by exceeding the boundaries set by the Nyquist-Shannon sampling theorem. 50 years after its first introduction in the scientific literature, wireless communications have significantly changed, but spectral efficiency remains one of the key challenges. To adopt FTN signaling, inter-symbol interference (ISI) patterns need to be equalized at the receiver. Motivated by the pattern recognition capabilities of convolutional neural networks with skip connections, we propose such deep learning architecture for ISI equalization and symbol demodulation in FTN receivers. We investigate the performance of the proposed model considering quadrature phase shift keying modulation and low density parity check coding, and compare it to a set of benchmarks, including frequency-domain equalization, a quadratic-programming-based receiver, and an equalization scheme based on a deep neural network. We show that our receiver outperforms any benchmark, achieving error rates comparable to those in additive white Gaussian noise channel, and higher effective throughput, thanks to the increased spectral efficiency of FTN signaling. With a compression factor of 60% and code rate 3/4, the proposed model achieves a peak effective throughput of 2.5 Mbps at just 10dB of energy per bit over noise power spectral density ratio, with other receivers being limited by error floors due to the strong inter-symbol interference. To promote reproducibility in deep learning for wireless communications, our code is open source at the repository provided in the references.