LNN-Powered Fluid Antenna Multiple Access

IEEE Access 2026 Open access

Read full text
Authors: Pedro D. Alvim, Hugerles S. Silva, Fernando D. A. García, Felipe A. P. Figueiredo, Rausley A. A. de Souza, Ugo S. Dias, Osamah S. Badarneh
DOI: 10.1109/access.2026.3685975
Cited by: 1

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 paper 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 achieves outage probability performance among the best-performing methods in the literature.

Fields of Study & Subjects

Antenna Design and Analysis Underwater Vehicles and Communication Systems Energy Harvesting in Wireless Networks

Publication details

Journal / source
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Volume
14
Pages
69471–69481
Publication date
1 January 2026
Language
EN