In recent years, beamforming has been vital to increasing the spectral and energy efficiency of the current and next-generation wireless communication systems, such as 5G, the Internet of Things (IoT), and beyond. In such a context, this letter extends the previously proposed single user, multiple-input single-output (SU-MISO) phase-transmittance radial basis function (PT-RBF) beamforming to self-organizing wireless network receivers with multiple users, multiple-input multiple-output (MU-MIMO) beamforming. In the proposed novel approach, the PT-RBF is designed to support multiple outputs and multiple layers, an innovation compared to previous shallow single output PT-RBF. On account of this deep neural network architecture, the proposed deep PT-RBF can handle multiple users with different modulation formats and distinct orders, presenting better results when compared either with other complex-valued neural networks or with the normalized least mean square algorithm.

中文翻译：

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用于多用户波束形成的深度相位传输 RBF 神经网络


近年来，波束成形对于提高当前和下一代无线通信系统（例如 5G、物联网 (IoT) 等）的频谱和能源效率至关重要。在这样的背景下，这封信将先前提出的单用户、多输入单输出（SU-MISO）相位透射率径向基函数（PT-RBF）波束成形扩展到具有多用户、多输入的自组织无线网络接收器。输入多输出（MU-MIMO）波束成形。在所提出的新方法中，PT-RBF 被设计为支持多个输出和多层，与之前的浅层单输出 PT-RBF 相比，这是一项创新。由于这种深度神经网络架构，所提出的深度 PT-RBF 可以处理具有不同调制格式和不同阶数的多个用户，与其他复值神经网络或归一化最小均方算法相比，呈现出更好的结果。