arXiv 2604.045892026-04-06cs.AIcs.LG

贪心与变压器多端口选择用于慢流体天线多址接入

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

University of A Coruña（拉科鲁尼亚大学）；CITIC（信息与通信技术研究中心）；Defense University Center at the Spanish Naval Academy（西班牙海军学院国防大学中心）；University of Granada（格拉纳达大学）；Research Centre for Information and Communication Technologies (CITIC-UGR)（信息与通信技术研究中心（CITIC-UGR））

Darian Perez-Adan, Jose P. Gonzalez-Coma, F. Javier Lopez-Martinez, Luis Castedo

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AI总结：

本文提出两种策略：贪心前向选择与基于Transformer的神经网络，提升流体天线多址接入的频谱效率。

摘要：

我们针对流体天线多址接入（FAMA）系统中的端口选择问题，提出两种互补策略：(i) GFwd+S，一种贪心前向选择方法，通过交换细化在频谱效率上优于现有方案；(ii) 基于Transformer的神经网络，通过模仿学习训练后采用Reinforce策略梯度阶段，以更低的计算成本接近GFwd+S性能。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。