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.
@article{arxiv.2507.08821,
title = {LNN-powered Fluid Antenna Multiple Access},
author = {Pedro D. Alvim and Hugerles S. Silva and Ugo S. Dias and Osamah S. Badarneh and Felipe A. P. Figueiredo and Rausley A. A. de Souza},
journal= {arXiv preprint arXiv:2507.08821},
year = {2025}
}