[2604.04589] Greedy and Transformer-Based Multi-Port Selection for Slow Fluid Antenna Multiple Access

Computer Science > Artificial Intelligence arXiv:2604.04589 (cs) [Submitted on 6 Apr 2026] Title:Greedy and Transformer-Based Multi-Port Selection for Slow Fluid Antenna Multiple Access Authors:Darian Perez-Adan, Jose P. Gonzalez-Coma, F. Javier Lopez-Martinez, Luis Castedo View a PDF of the paper titled Greedy and Transformer-Based Multi-Port Selection for Slow Fluid Antenna Multiple Access, by Darian Perez-Adan and 3 other authors View PDF HTML (experimental) Abstract: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. Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2604.04589 [cs.AI]   (or arXiv:2604.04589v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.04589 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Darian Perez-Adan [view email] [v1] Mon, 6 Apr 2026 10:5...