Hybrid beamforming is vital in modern wireless systems, especially for massive MIMO and millimeter-wave deployments, offering efficient directional transmission with reduced hardware complexity. However, effective beamforming in multi-user scenarios relies heavily on accurate channel state information, the acquisition of which often incurs excessive pilot overhead, degrading system performance. To address this and inspired by the spatial congruence between sub-6GHz (sub-6G) and mmWave channels, we propose a Sub-6G information Aided Multi-User Hybrid Beamforming (SA-MUHBF) framework, avoiding excessive use of pilots. SA-MUHBF employs a convolutional neural network to predict mmWave beamspace from sub-6G channel estimate, followed by a novel multi-layer graph neural network for analog beam selection and a linear minimum mean-square error algorithm for digital beamforming. Numerical results demonstrate that SA-MUHBF efficiently predicts the mmWave beamspace representation and achieves superior spectrum efficiency over state-of-the-art benchmarks. Moreover, SA-MUHBF demonstrates robust performance across varied sub-6G system configurations and exhibits strong generalization to unseen scenarios.

翻译：混合波束赋形在现代无线系统中至关重要，尤其是在大规模MIMO和毫米波部署中，能够以较低的硬件复杂度实现高效定向传输。然而，多用户场景下的高效波束赋形高度依赖于精确的信道状态信息，其获取过程往往会产生过多的导频开销，从而降低系统性能。为解决这一问题并受sub-6GHz与毫米波信道空间一致性的启发，我们提出了一种基于Sub-6G信息辅助的多用户混合波束赋形（SA-MUHBF）框架，避免了导频的过度使用。SA-MUHBF采用卷积神经网络从sub-6G信道估计中预测毫米波波束空间，随后通过新型多层图神经网络进行模拟波束选择，并利用线性最小均方误差算法实现数字波束赋形。数值结果表明，SA-MUHBF能够高效预测毫米波波束空间表示，并在频谱效率上优于现有先进基准方案。此外，SA-MUHBF在不同sub-6G系统配置下均表现出稳健性能，并对未见场景展现出强大的泛化能力。