用液态神经网络优化流式天线多路接入的端口选择。
LNN-powered Fluid Antenna Multiple Access
- 将端口选择建模为多标签分类问题，提升有限观测下的选优能力。
- 在α-μ衰落模型下，相比现有方法显著降低中断概率。
- 通过超参优化适配不同观测场景，适合无线通信系统设计者。
流式天线系统是无线通信中一种创新方法，最近被用于多路接入以通过端口选择优化信号干扰噪声比。本文首次将端口选择问题建模为多标签分类任务，在有限端口观测条件下提升最优端口选择性能。我们利用液态神经网络（LNN）预测在新兴流式天线多路接入场景中的最优端口，同时适用于更通用的α-μ衰落模型。通过超参数优化进一步改进LNN架构以适应不同观测条件。实验结果表明，该方法相较于现有方法具有更低的中断概率。
原文摘要 · Abstract (English)
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 $α$-$μ$ 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.
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