MetNet: A Novel Low-Complexity Neural Network-Aided Detection for Faster-Than-Nyquist (FTN) Signaling in ISI Channels

This paper studies the application of neural networks to Viterbi detection of FTN signals in an intersymbol interference (ISI) channel. The main contribution of this paper is to propose a receiver structure for detecting FTN signals in unknown static ISI channel. In particular, we propose a novel lo...

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Bibliographic Details

Published in:	IEEE Open Journal of the Communications Society
Main Authors:	Ammar Abdelsamie, Ian Marsland, Ahmed Ibrahim, Halim Yanikomeroglu
Format:	Article
Language:	English
Published:	IEEE 2023-01-01
Subjects:	Faster-than-Nyquist maximum likelihood sequence estimation AI based signal detection spectral efficiency enhancement intersymbol interference
Online Access:	https://ieeexplore.ieee.org/document/10071549/