FTN-Based Non-Orthogonal Signal Detection Technique With Machine Learning in Quasi-Static Multipath Channel

Faster-than-Nyquist (FTN) signaling is a useful communication technique for high spectral efficiency. However, since orthogonality between symbols is destroyed when a symbol rate faster than the Nyquist rate is used, intersymbol interference (ISI) is inevitably generated. Interference cancellation a...

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Bibliographic Details
Published in:IEEE transactions on broadcasting 2024-03, Vol.70 (1), p.1-9
Main Authors: Baek, Myung-Sun, Jung, Eui-Suk, Park, Young Soo, Lee, Yong-Tae
Format: Article
Language:English
Subjects:
Algorithms
Codes
Computer simulation
Deep learning
Fading
Fading channels
FTN signaling
Interference
Interference cancellation
LSTM
Machine learning
Multipath channels
Orthogonality
Recurrent neural networks
RNN
Signal detection
Signal processing
Signaling
Symbols
Telecommunications
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Summary:Faster-than-Nyquist (FTN) signaling is a useful communication technique for high spectral efficiency. However, since orthogonality between symbols is destroyed when a symbol rate faster than the Nyquist rate is used, intersymbol interference (ISI) is inevitably generated. Interference cancellation and signal detection processes are required to reduce the effect of ISI on the FTN receiver. Furthermore, FTN signaling in the multipath fading channel is a complicated problem because nonlinear interference from FTN signaling is combined with the additional nonlinear interference from the multipath fading channel. This paper proposes a deep learning-based signal detection technique for the FTN signal received through a multipath fading channel. The proposed technology considers the normal FTN signaling-based transmitter without additional signal processing schemes such as precoding and power allocation. We designed a recurrent neural network (RNN)-based deep learning structure and applied it to the FTN signaling-based system. For the RNN structure, both unidirectional long short-term memory (Uni-LSTM) and bidirectional LSTM (Bi-LSTM) architectures were considered. Simulation results showed that both Uni-LSTM and Bi-LSTM performed better than the conventional BCJR algorithm when the FTN factor \tau = 0.8 and 0.7. Furthermore, the Bi-LSTM architecture achieved the best performances in all simulations. The proposed LSTM-based FTN detector can detect transmitted symbols without estimating channel coefficients and SNR.
ISSN:0018-9316
1557-9611
DOI:10.1109/TBC.2023.3291135