# LNN-powered Fluid Antenna Multiple Access

arXiv: https://arxiv.org/abs/2507.08821 · HTML: https://mlsanity.com/paper/2507.08821/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Liquid Time-constant Networks](https://mlsanity.com/paper/2006.04439/) (2020)
- [Fluid Antenna Systems](https://mlsanity.com/paper/2005.11561/) (2020)
- [Port Selection for Fluid Antenna Systems](https://mlsanity.com/paper/2201.05702/) (2022)
- [A New Analytical Approximation of the Fluid Antenna System Channel](https://mlsanity.com/paper/2203.09318/) (2022)
- [On Performance of Fluid Antenna System using Maximum Ratio Combining](https://mlsanity.com/paper/2309.07582/) (2023)
- [Robust Continuous-Time Beam Tracking with Liquid Neural Network](https://mlsanity.com/paper/2405.00365/) (2024)

## Abstract (arXiv)

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.