LNN-powered Fluid Antenna Multiple Access
This work addresses wireless communication optimization for multiple access scenarios, representing an incremental improvement with specific gains.
The paper tackles the port selection problem in fluid antenna multiple access by framing it as a multi-label classification task, using liquid neural networks (LNNs) to predict optimal ports under a general fading model, resulting in lower outage probability values than existing methods.
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.