Channel-Agnostic Training of Transmitter and Receiver for Wireless Communications
Article
Article Title | Channel-Agnostic Training of Transmitter and Receiver for Wireless Communications |
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ERA Journal ID | 34304 |
Article Category | Article |
Authors | Davey, Christopher P., Shakeel, Ismail, Deo, Ravinesh C. and Salcedo-sanz, Sancho |
Journal Title | Sensors |
Journal Citation | 23 (24) |
Article Number | 9848 |
Number of Pages | 21 |
Year | 2023 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1424-8220 |
1424-8239 | |
Digital Object Identifier (DOI) | https://doi.org/10.3390/s23249848 |
Web Address (URL) | https://www.mdpi.com/1424-8220/23/24/9848 |
Abstract | Wireless communications systems are traditionally designed by independently optimising signal processing functions based on a mathematical model. Deep learning-enabled communications have demonstrated end-to-end design by jointly optimising all components with respect to the communications environment. In the end-to-end approach, an assumed channel model is necessary to support training of the transmitter and receiver. This limitation has motivated recent work on over-the-air training to explore disjoint training for the transmitter and receiver without an assumed channel. These methods approximate the channel through a generative adversarial model or perform gradient approximation through reinforcement learning or similar methods. However, the generative adversarial model adds complexity by requiring an additional discriminator during training, while reinforcement learning methods require multiple forward passes to approximate the gradient and are sensitive to high variance in the error signal. A third, collaborative agent-based approach relies on an echo protocol to conduct training without channel assumptions. However, the coordination between agents increases the complexity and channel usage during training. In this article, we propose a simpler approach for disjoint training in which a local receiver model approximates the remote receiver model and is used to train the local transmitter. This simplified approach performs well under several different channel conditions, has equivalent performance to end-to-end training, and is well suited to adaptation to changing channel environments. |
Keywords | deep learning; channel free training; wireless communications; over-the-air training; neural networks |
Related Output | |
Is part of | Applied Deep Learning for Artificial Intelligence-enabled Wireless Communication |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400602. Data communications |
Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
Byline Affiliations | School of Mathematics, Physics and Computing |
Defence Science and Technology Group, Australia | |
Institute for Advanced Engineering and Space Sciences | |
Centre for Applied Climate Sciences | |
Centre for Health Research | |
University of Alcala, Spain |
https://research.usq.edu.au/item/zq1z7/channel-agnostic-training-of-transmitter-and-receiver-for-wireless-communications
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