In an aerial hybrid massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) system, how to design a spectral-efficient broadband multi-user hybrid beamforming with a limited pilot and feedback overhead is challenging. To this end, by modeling the key transmission modules as an end-to-end (E2E) neural network, this paper proposes a data-driven deep learning (DL)-based unified hybrid beamforming framework for both the time division duplex (TDD) and frequency division duplex (FDD) systems with implicit channel state information (CSI). For TDD systems, the proposed DL-based approach jointly models the uplink pilot combining and downlink hybrid beamforming modules as an E2E neural network. While for FDD systems, we jointly model the downlink pilot transmission, uplink CSI feedback, and downlink hybrid beamforming modules as an E2E neural network. Different from conventional approaches separately processing different modules, the proposed solution simultaneously optimizes all modules with the sum rate as the optimization object. Therefore, by perceiving the inherent property of air-to-ground massive MIMO-OFDM channel samples, the DL-based E2E neural network can establish the mapping function from the channel to the beamformer, so that the explicit channel reconstruction can be avoided with reduced pilot and feedback overhead. Besides, practical low-resolution phase shifters (PSs) introduce the quantization constraint, leading to the intractable gradient backpropagation when training the neural network. To mitigate the performance loss caused by the phase quantization error, we adopt the transfer learning strategy to further fine-tune the E2E neural network based on a pre-trained network that assumes the ideal infinite-resolution PSs. Numerical results show that our DL-based schemes have considerable advantages over state-of-the-art schemes.
翻译:在空中混合型大型多输出多输出(IMIM)和正方位频率多输出(OFDM)系统中,如何设计一个光速高效宽带多用户混合波束且试点和反馈管理有限。为此,通过将关键传输模块建模为端对端神经网络(E2E),本文件提议为时分双截(TDD)和频分双曲(DFD)系统设计一个基于数据驱动的深度学习(DL)统一混合波形组合框架,并含隐含频道状态反馈信息(CSI)系统。对于TDD系统,基于DL的拟议模式联合模型将上行高效宽带多用户混合波束与有限的试点和反馈管理管理管理管理模式混合,作为E2E2级试验和下行导导神经网络网络网络网络网络网络网络网络网络网络网络的连接和下行模式。我们联合建模下行模式,将CSI的反馈和下行混合模式建成基于E2E2E的神经网络网络网络。不同于传统方法,拟议解决方案同时优化所有模块与精细数据转换为优化目的。因此,IMO级网络升级升级的网络的网络的深度运行导致内部磁路路路路路将降低运行运行运行的功能可以将内软化。