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Lookup NU author(s): Dr Tongyang XuORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 The Author(s)Artificial intelligence (AI) is enabling intelligent communications where learning based signal classification simplifies optical network signal allocation and shifts signal processing pressure to each network edge. This work proposes a non-orthogonal signal waveform framework that leverages its unique spectral compression characteristic as a user address for efficiently forwarding messages to target users. The primary focus of this work lies in the physical layer intelligent receiver design, which can automatically identify different received signal formats without preamble notification in a non-cooperative communication approach. Traditional signal classification methods, such as convolutional neural network (CNN), rely on extensive training, resulting in a heavy dependency on large training datasets. To overcome this limitation, this work designs a specific two-layer scattering neural network that can accurately separate signals even when the training data is limited, leading to reduced training complexity. Its performance remains robust in diverse transmission conditions. Furthermore, the scattering neural network is interpretable because features are extracted based on deterministic wavelet filters rather than training based filters.
Author(s): Chen Y, Xu T, Xu T
Publication type: Article
Publication status: Published
Journal: Optics Communications
Year: 2024
Volume: 560
Print publication date: 01/06/2024
Online publication date: 13/04/2024
Acceptance date: 11/03/2024
Date deposited: 10/04/2024
ISSN (print): 0030-4018
ISSN (electronic): 1873-0310
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.optcom.2024.130465
DOI: 10.1016/j.optcom.2024.130465
Data Access Statement: No data was used for the research described in the article
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