标题： 基于LNN的流体天线多址接入 显示英文标题

标题： LNN-powered Fluid Antenna Multiple Access

摘要： 流体天线系统代表了无线通信中的一种创新方法，最近被应用于多址接入，通过端口选择优化信干噪比。 这封信首次将端口选择问题框架化为多标签分类任务，通过有限的端口观测改进最佳端口选择。 我们通过利用液态神经网络（LNNs）来预测在新兴流体天线多址接入场景下的最优端口，同时结合更一般的 $\alpha$-$\mu$ 衰落模型来解决这一挑战。 我们还应用超参数优化来优化不同观测场景下的LNN架构。 我们的方法产生的中断概率值低于现有方法。 显示英文摘要

摘要： 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 $\alpha$-$\mu$ 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.