提出两种新方法，提升流体天线多接入系统的端口选择效率。
Greedy and Transformer-Based Multi-Port Selection for Slow Fluid Antenna Multiple Access
- 采用贪心前向选择加交换优化，提升性能
- 基于Transformer的神经网络逼近最优解，计算成本更低
- 适合追求高吞吐与低延迟的无线系统设计
针对具有多端口流体天线（FA）接收机的流体天线多接入（FAMA）系统中的端口选择问题，现有方法或以极高计算成本实现近似最优频谱效率（SE），或为降低复杂度而显著牺牲性能。本文提出两种互补策略：(i) GFwd+S，一种贪心前向选择结合交换优化的方法，在频谱效率上持续优于当前最优基准方案；(ii) 一种通过模仿学习训练、再经Reinforce策略梯度优化的Transformer神经网络，以更低计算开销逼近GFwd+S的性能表现。
原文摘要 · Abstract (English)
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.
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