Receiver Design for Faster-Than-Nyquist Signaling: Deep-Learning-Based Architectures

Receiver Design for Faster-Than-Nyquist Signaling: Deep-Learning-Based Architectures

Faster-than-Nyquist (FTN) is a promising paradigm to improve bandwidth utilization at the expense of additional intersymbol interference (ISI). In this paper, we apply state-of-the-art deep learning (DL) technology into receiver design for FTN signaling and propose two DL-based new architectures. Fi...

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Bibliographic Details
Main Authors: Peiyang Song, Fengkui Gong, Qiang Li, Guo Li, Haiyang Ding
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Faster-than-Nyquist
receiver design
signal detection
deep learning
intersymbol interference
channel coding
Online Access:https://ieeexplore.ieee.org/document/9060948/

Internet

https://ieeexplore.ieee.org/document/9060948/

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