AI 中文总结
本文针对采用动态超表面天线的多用户下行链路系统，提出基于深度展开的快速三混合波束成形框架，利用图神经网络实现算法加速，提升和速率的同时缩短运行时间，具备良好扩展性与鲁棒性。
AI 中文摘要
三混合多输入多输出架构近期成为下一代无线系统的有潜力支撑技术，因其能在不按比例增加硬件成本或功耗的情况下提供更高的设计灵活性。然而，由此产生的三域耦合使波束成形优化具有挑战性且计算量大。本文针对采用动态超表面天线（DMA）的多用户下行链路系统，提出一种快速三混合波束成形框架。基于加权和速率最大化与加权和最小均方误差最小化的等价性，首先在每个DMA输入功率约束下推导了一种迭代算法，其所有更新方程均为闭式形式，但收敛速度固有较慢。为实现实时运行，该算法进一步被展开为可训练的有限迭代架构，采用图神经网络，其确保排列等变性并支持不同数量的用户。在射线追踪信道数据上训练后，该展开方法实现了相当或更高的系统和速率，同时将运行时间缩短了一个数量级以上。该方法还在各种环境中表现出强可扩展性、鲁棒性和泛化能力。
英文摘要
Tri-hybrid multiple-input multiple-output architectures have recently emerged as a promising enabler for next-generation wireless systems, as they potentially provide enhanced design flexibility without a proportional increase in hardware cost or power consumption. However, the resulting triple-domain coupling renders beamforming optimization challenging and computationally demanding. This paper develops a fast tri-hybrid beamforming framework for multiuser downlink systems employing dynamic metasurface antennas (DMAs). Based on the equivalence between weighted sum-rate maximization and weighted sum-minimum mean square error minimization, an iterative algorithm is first derived under per-DMA input power constraints, with all update equations available in closed form, but convergence inherently remains slow. To enable real-time operation, the algorithm is further unfolded into a trainable finite-iteration architecture using graph neural networks that ensure permutation equivariance and support varying numbers of users. Trained on ray-tracing channel data, the unfolded method achieves comparable or higher system sum-rates while reducing runtime by more than an order of magnitude. The method also demonstrates strong scalability, robustness, and generalization across various environments.
