Beamforming with large-scale antenna arrays has been widely used in recent years, which is acknowledged as an important part in 5G and incoming 6G. Thus, various techniques are leveraged to improve its performance, e.g., deep learning, advanced optimization algorithms, etc. Although its performance in many previous research scenarios with deep learning is quite attractive, usually it drops rapidly when the environment or dataset is changed. Therefore, designing effective beamforming network with strong robustness is an open issue for the intelligent wireless communications. In this paper, we propose a robust beamforming self-supervised network, and verify it in two kinds of different datasets with various scenarios. Simulation results show that the proposed self-supervised network with hybrid learning performs well in both classic DeepMIMO and new WAIR-D dataset with the strong robustness under the various environments. Also, we present the principle to explain the rationality of this kind of hybrid learning, which is instructive to apply with more kinds of datasets.

翻译：近年来，大规模天线阵列波束赋形技术被广泛应用于5G及未来6G通信中，被视为关键组成部分。因此，多种技术手段被用于提升其性能，例如深度学习、先进优化算法等。尽管深度学习在众多先前研究场景中的性能表现颇具吸引力，但当环境或数据集发生变化时，其性能通常会急剧下降。因此，设计具有强鲁棒性的高效波束赋形网络仍是智能无线通信领域的一个开放性问题。本文提出了一种鲁棒波束赋形自监督网络，并在两种不同场景的数据集上进行了验证。仿真结果表明，所提出的混合学习自监督网络在经典DeepMIMO数据集和新型WAIR-D数据集上均表现优异，在多种环境下展现出强鲁棒性。此外，我们提出了用于解释此类混合学习合理性的原理，这为将其应用于更多类型的数据集提供了指导性意义。