End-to-end learning of adaptive coded modulation schemes for resilient wireless communications
Article
Davey, Christopher P., Shakeel, Ismail, Deo, Ravinesh C., Sharma, Ekta, Salcedo-sanz, Sancho and Soar, Jeffrey. 2024. "End-to-end learning of adaptive coded modulation schemes for resilient wireless communications." Applied Soft Computing. 159. https://doi.org/10.1016/j.asoc.2024.111672
| Article Title | End-to-end learning of adaptive coded modulation schemes for resilient wireless communications |
|---|---|
| ERA Journal ID | 17759 |
| Article Category | Article |
| Authors | Davey, Christopher P., Shakeel, Ismail, Deo, Ravinesh C., Sharma, Ekta, Salcedo-sanz, Sancho and Soar, Jeffrey |
| Journal Title | Applied Soft Computing |
| Journal Citation | 159 |
| Article Number | 111672 |
| Number of Pages | 18 |
| Year | 2024 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 1568-4946 |
| 1872-9681 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.asoc.2024.111672 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1568494624004460 |
| Abstract | Adaptive modulation and coding schemes play a crucial role in ensuring robust data transfer in wireless communications, especially when faced with changes or interference in the transmission channel. These schemes involve the use of variable coding rates, which can be achieved normally through code puncturing or shortening, and have been adopted in 4G and 5G communication standards. In recent works, auto-encoders for wireless communications have demonstrated the ability to learn short code representations that achieve gains over conventional codes. Such a methodology is attractive as it can learn optimal representations under a variety of channel conditions. However, due to its structure the auto-encoder does not currently support multiple code rates with a single model. This article draws upon the discipline of multi-task learning, as it applies to deep learning and therefore devises a branching architecture for the auto-encoder and custom training algorithm in training transmitter and receiver for adaptive modulation and coding. In this article we aim to demonstrate improvements in Block Error Rate over conventional methods in the Additive White Gaussian Noise channel, and to analyse the performance of the model under Rayleigh fading channels without retraining the auto-encoder on the new channel. This article demonstrates a novel approach towards training auto-encoder models to jointly learn adaptive modulation and coding schemes framed as a multi-task learning problem. The research outcomes extend end-to-end learning approaches to the design of adaptive wireless communications systems. |
| Keywords | Adaptation; Wireless communications; Coding design; Deep learning; Multi-task learning |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400608. Wireless communication systems and technologies (incl. microwave and millimetrewave) |
| Byline Affiliations | School of Mathematics, Physics and Computing |
| Defence Science and Technology Group, Australia | |
| University of Alcala, Spain | |
| School of Business |
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https://research.usq.edu.au/item/z849y/end-to-end-learning-of-adaptive-coded-modulation-schemes-for-resilient-wireless-communications
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