LNN-powered Fluid Antenna Multiple Access
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915385465372672 |
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| author | Alvim, Pedro D. Silva, Hugerles S. Dias, Ugo S. Badarneh, Osamah S. Figueiredo, Felipe A. P. de Souza, Rausley A. A. |
| author_facet | Alvim, Pedro D. Silva, Hugerles S. Dias, Ugo S. Badarneh, Osamah S. Figueiredo, Felipe A. P. de Souza, Rausley A. A. |
| contents | Fluid antenna systems represent an innovative approach in wireless communication, recently applied in multiple access to optimize the signal-to-interference-plus-noise ratio through port selection. This letter frames the port selection problem as a multi-label classification task for the first time, improving best-port selection with limited port observations. We address this challenge by leveraging liquid neural networks (LNNs) to predict the optimal port under emerging fluid antenna multiple access scenarios alongside a more general $α$-$μ$ fading model. We also apply hyperparameter optimization to refine LNN architectures for different observation scenarios. Our approach yields lower outage probability values than existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08821 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LNN-powered Fluid Antenna Multiple Access Alvim, Pedro D. Silva, Hugerles S. Dias, Ugo S. Badarneh, Osamah S. Figueiredo, Felipe A. P. de Souza, Rausley A. A. Signal Processing Information Theory Machine Learning Fluid antenna systems represent an innovative approach in wireless communication, recently applied in multiple access to optimize the signal-to-interference-plus-noise ratio through port selection. This letter frames the port selection problem as a multi-label classification task for the first time, improving best-port selection with limited port observations. We address this challenge by leveraging liquid neural networks (LNNs) to predict the optimal port under emerging fluid antenna multiple access scenarios alongside a more general $α$-$μ$ fading model. We also apply hyperparameter optimization to refine LNN architectures for different observation scenarios. Our approach yields lower outage probability values than existing methods. |
| title | LNN-powered Fluid Antenna Multiple Access |
| topic | Signal Processing Information Theory Machine Learning |
| url | https://arxiv.org/abs/2507.08821 |