Robust beamforming is a pivotal technique in massive multiple-input multiple-output (MIMO) systems as it mitigates interference among user equipment (UE). One current risk-neutral approach to robust beamforming is the stochastic weighted minimum mean square error method (WMMSE). However, this method necessitates statistical channel information, which is typically inaccessible, particularly in fifth-generation new radio frequency division duplex cellular systems with limited feedback. To tackle this challenge, we propose a novel approach that leverages a channel variational auto-encoder (CVAE) to simulate channel behaviors using limited feedback, eliminating the need for specific distribution assumptions present in existing methods. To seamlessly integrate model learning into practical wireless communication systems, this paper introduces two learning strategies to prepare the CVAE model for practical deployment. Firstly, motivated by the digital twin technology, we advocate employing a high-performance channel simulator to generate training data, enabling pretraining of the proposed CVAE while ensuring non-disruption to the practical wireless communication system. Moreover, we present an alternative online method for CVAE learning, where online training data is sourced based on channel estimations using Type II codebook. Numerical results demonstrate the effectiveness of these strategies, highlighting their exceptional performance in channel generation and robust beamforming applications.

翻译：鲁棒波束赋形是大规模多输入多输出（MIMO）系统中抑制用户设备间干扰的关键技术。当前一种风险中性的鲁棒波束赋形方法是随机加权最小均方误差法（WMMSE），但该方法需要统计信道信息，而在第五代新无线电频分双工蜂窝系统等有限反馈场景下，此类信息通常难以获取。为解决这一挑战，我们提出一种新方法，利用信道变分自编码器（CVAE）通过有限反馈模拟信道行为，无需像现有方法那样假设特定分布。为将模型学习无缝集成到实际无线通信系统中，本文引入两种学习策略以准备CVAE模型用于实际部署。首先，受数字孪生技术启发，我们主张采用高性能信道模拟器生成训练数据，从而在确保不干扰实际无线通信系统的前提下，实现所提CVAE的预训练。此外，我们提出一种替代的CVAE在线学习方法，其在线训练数据基于使用Type II码本的信道估计获取。数值结果验证了这些策略的有效性，凸显了其在信道生成与鲁棒波束赋形应用中的卓越性能。