Problem
Research questions and friction points this paper is trying to address.
Beamforming
Port Selection
Multi-Cell Networks
Federated Learning
Weighted Sum-Rate
Joint Beamforming Design and Port Selection in Fluid Antenna-Assisted Multi-Cell Networks: A Personalized Federated Learning Approach
🤖 AI Summary
本文通过一种基于位置感知双分支深度神经网络的联邦表示学习方法，解决了多小区流天线辅助网络中的联合波束成形设计和端口选择问题，以最大化加权和速率。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Federated Representation Learning
Position-aware Dual-branch DNN
Joint Beamforming and Port Selection
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