基于学习的连续孔径阵列系统能量效率波束成形
Learning-Based Beamforming for Energy Efficiency of Continuous Aperture Array Systems
AI总结：
研究针对下行多用户多连续孔径阵列系统，提出由图神经网络和基于泛函梯度的隐式神经表示组成的级联网络架构，联合优化基站连续孔径阵列尺寸与波束成形函数，提升能量效率，降低推理延迟、样本复杂度和训练时间。
AI中文摘要：
本文联合优化基站（BS）连续孔径阵列（CAPA）尺寸和波束成形函数，以最大化下行多用户多CAPA系统的能量效率（EE），其中BS和用户均配备CAPA。由于波束成形函数是BS CAPA上的连续电流分布，EE最大化问题是一个将孔径大小和波束成形设计耦合的非平凡泛函优化问题。为应对这一挑战，我们提出了一种由图神经网络（GNN）和基于泛函梯度的隐式神经表示（FGB-INR）组成的级联网络架构，分别学习BS CAPA尺寸和波束成形函数。两个网络都利用了最优优化策略的排列不变性，FGB-INR的更新方程根据EE目标的泛函梯度结构设计。仿真结果表明，该方法在大幅降低推理延迟的同时接近数值方法的EE。结果还表明，FGB-INR中的泛函梯度结构在降低样本复杂度和训练时间的同时提高了EE。
英文摘要：
This paper jointly optimizes the base-station (BS) continuous aperture array (CAPA) dimensions and beamforming functions to maximize energy efficiency (EE) of the downlink multiuser multi-CAPA system, where both the BS and the users are equipped with CAPAs. Since the beamforming functions are continuous current distribution over the BS CAPA, the resulting EE maximization problem is a nontrivial functional optimization problem that couples aperture sizing and beamforming design. To address this challenge, we propose a cascaded network architecture consisting of a graph neural network (GNN) and a functional-gradient based implicit neural representation (FGB-INR) to learn the BS CAPA dimensions and beamforming functions, respectively. Both networks exploit the permutation equivariance of the optimal optimization policy, and the update equations of FGB-INR are designed according to the functional-gradient structure of the EE objective. Simulation results show that the proposed method approaches the EE of the numerical method while substantially reducing inference latency. They also demonstrates that the functional-gradient structure in FGB-INR improves EE while reducing sample complexity and training time.
