AI 中文总结
针对流体天线辅助多小区网络的联合波束成形与端口选择问题，提出FedRep框架结合PA-DNN，通过共享全局波束成形参数、保留本地端口选择参数优化，可实现更高加权和速率。
AI 中文摘要
本文研究流体天线辅助（FAS）多小区网络中的联合波束成形与端口选择问题。在这类网络中，主动波束成形与离散FA端口选择通过小区内和小区间干扰耦合，需联合优化以最大化加权和速率（WSR）。我们开发了一种具有位置感知双分支深度神经网络（PA-DNN）的联邦表示学习（FedRep）框架，PA-DNN以信道状态信息和端口位置编码为输入，通过两个任务特定分支联合输出波束成形向量与端口选择结果。为支持异构小区间的分布式训练，FedRep框架在基站间共享与波束成形相关的全局参数，同时保留端口选择参数为小区本地以实现个性化适配。仿真结果表明，所提方案相比传统联邦学习（FL）及端口选择基准方案，能实现更高的加权和速率。
英文摘要
This paper investigates joint beamforming and port selection in multi-cell fluid antenna-assisted (FAS) networks. In such networks, active beamforming and discrete FA port selection are coupled through intra-cell and inter-cell interference and are jointly optimized to maximize the weighted sum-rate (WSR). We develop a federated representation learning (FedRep) framework with a position-aware dual-branch deep neural network (PA-DNN). The PA-DNN uses channel state information and port positional encoding as inputs, and jointly outputs beamforming vectors and port selections through two task-specific branches. To support decentralized training across heterogeneous cells, the FedRep framework shares global beamforming-related parameters among base stations while keeping port-selection parameters local for cell-specific adaptation. Simulation results show that the proposed scheme achieves a higher weighted sum-rate than conventional FL and port-selection benchmark schemes.
CommentsThis paper is accepted by IEEE GLOBECOM 2026
