Deep Learning-Based Auto-Encoder for Time-Offset Sub-Faster-Than-Nyquist Downlink NOMA With Timing Errors and Imperfect CSI

This paper presents architecture designs and performance evaluations for the encoding and decoding of transmitted and received sequences for downlink time-offset sub-faster-than-Nyquist non-orthogonal multiple access signaling (TO-sFTN-NOMA). A conventional singular value decomposition (SVD)-based s...

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Veröffentlicht in:	IEEE journal of selected topics in signal processing Jg. 18; H. 7; S. 1178 - 1193
Hauptverfasser:	Aboutaleb, Ahmed, Torabi, Mohammad, Belzer, Benjamin, Sivakumar, Krishnamoorthy
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Architecture Artificial neural networks Asynchronous transmission auto-encoder Autoencoders Bit error rate Coders Coding Complexity Convolutional neural networks Decoding Deep learning Downlink Downlinking faster-than Nyquist signaling Interference cancellation Machine learning Neural networks NOMA non-orthogonal multiple access Nonorthogonal multiple access Performance evaluation Singular value decomposition Symbols Timing
ISSN:	1932-4553, 1941-0484
Online-Zugang:	Volltext
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