标题： 面向实用流体天线系统的研究：协同优化端口选择和波束成形的硬件和软件 显示英文标题

标题： Toward Practical Fluid Antenna Systems: Co-Optimizing Hardware and Software for Port Selection and Beamforming

摘要： 本文提出了一种软硬件协同设计方法，以高效优化流体天线系统（FASs）中的波束成形和端口选择。 首先，对一个基于流体天线（FA）的下行链路多小区多输入多输出（MIMO）网络进行了建模，并提出了一个加权和速率（WSR）最大化问题。 其次，提出了一种将图神经网络（GNNs）与随机端口选择（RPS）相结合的方法，以联合优化波束成形和端口选择，同时评估随机选择的优缺点。 第三，开发了一个基于现场可编程门阵列（FPGA）的指令驱动深度学习加速器，以最小化推理延迟。 为了进一步提高效率，引入了一个调度算法，以减少冗余计算并最小化计算核心的空闲时间。 仿真结果表明，所提出的GNN-RPS方法实现了具有竞争力的通信性能。 此外，实验评估表明，基于FPGA的加速器在同时执行多个端口选择的波束成形推理时保持了低延迟。 显示英文摘要

摘要： This paper proposes a hardware-software co-design approach to efficiently optimize beamforming and port selection in fluid antenna systems (FASs). To begin with, a fluid-antenna (FA)-enabled downlink multi-cell multiple-input multiple-output (MIMO) network is modeled, and a weighted sum-rate (WSR) maximization problem is formulated. Second, a method that integrates graph neural networks (GNNs) with random port selection (RPS) is proposed to jointly optimize beamforming and port selection, while also assessing the benefits and limitations of random selection. Third, an instruction-driven deep learning accelerator based on a field-programmable gate array (FPGA) is developed to minimize inference latency. To further enhance efficiency, a scheduling algorithm is introduced to reduce redundant computations and minimize the idle time of computing cores. Simulation results demonstrate that the proposed GNN-RPS approach achieves competitive communication performance. Furthermore, experimental evaluations indicate that the FPGA-based accelerator maintains low latency while simultaneously executing beamforming inference for multiple port selections.