LNN-powered Fluid Antenna Multiple Access

TL;DR

This paper introduces a novel fluid antenna multiple access method that uses liquid neural networks to improve port selection, reducing outage probability in wireless communication systems.

Contribution

It is the first to frame port selection as a multi-label classification problem and applies LNNs with hyperparameter tuning for enhanced performance.

Findings

Lower outage probability compared to existing methods

Effective port selection with limited observations

Improved performance under $\alpha$-$\mu$ fading model

Abstract

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 $\alpha$-$\mu$ 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.