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...

Volledige beschrijving

Bibliografische gegevens

Hoofdauteurs:	Ammar Abdelsamie, Ian Marsland, Ahmed Ibrahim, Halim Yanikomeroglu
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	IEEE 2023-01-01
Reeks:	IEEE Open Journal of the Communications Society
Onderwerpen:	Faster-than-Nyquist maximum likelihood sequence estimation AI based signal detection spectral efficiency enhancement intersymbol interference
Online toegang:	https://ieeexplore.ieee.org/document/10071549/
Tags:	Voeg label toe Geen labels, Wees de eerste die dit record labelt!