Hybrid beamforming can provide rapid data transmission rates while reducing the complexity and cost of massive multiple-input multiple-output (MIMO) systems. However, channel state information (CSI) is imperfect in realistic downlink channels, introducing challenges to hybrid beamforming (HBF) design. This letter proposes an unsupervised deep learning neural network (USDNN) for hybrid beamforming to prevent the labeling overhead of supervised learning and improve the achievable sum rate based on imperfect CSI. The simulation results show that our proposed method is 74% better than MO and 120% better than orthogonal match pursuit (OMP) systems; our proposed USDNN can achieve near-optimal performance.

中文翻译：

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基于无监督深度学习网络的混合波束成形，用于具有不完美 CSI 的下行信道

混合波束成形可以提供快速的数据传输速率，同时降低大规模多输入多输出 (MIMO) 系统的复杂性和成本。然而，实际下行链路信道中的信道状态信息 (CSI) 并不完善，这给混合波束成形 (HBF) 设计带来了挑战。这封信提出了一种用于混合波束成形的无监督深度学习神经网络 (USDNN)，以防止监督学习的标记开销，并提高基于不完美 CSI 的可实现总和率。仿真结果表明，我们提出的方法比 MO 好 74%，比正交匹配追踪 (OMP) 系统好 120%；我们提出的 USDNN 可以达到接近最佳的性能。