# Greedy and Transformer-Based Multi-Port Selection for Slow Fluid Antenna Multiple Access

arXiv: https://arxiv.org/abs/2604.04589 · HTML: https://mlsanity.com/paper/2604.04589/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 6 Apr 2026.

## Abstract (arXiv)

We address the port-selection problem in fluid antenna multiple access (FAMA) systems with multi-port fluid antenna (FA) receivers. Existing methods either achieve near-optimal spectral efficiency (SE) at prohibitive computational cost or sacrifice significant performance for lower complexity. We propose two complementary strategies: (i) GFwd+S, a greedy forward-selection method with swap refinement that consistently outperforms state-of-the-art reference schemes in terms of SE, and (ii) a Transformer-based neural network trained via imitation learning followed by a Reinforce policy-gradient stage, which approaches GFwd+S performance at lower computational cost.