端到端联合估计信道并设计混合波束成形，兼顾硬件缺陷。
Model-based learning for joint channel estimationand hybrid MIMO precoding
- 基于可展开的匹配追踪与投影梯度法，模型驱动实现信道估计与波束成形
- 在真实合成信道上验证，性能接近理想情况，仅需少量可学习参数
- 适合资源受限场景，兼具轻量、可解释性与实际系统适应性
混合波束成形是实现成本可控的大规模多输入多输出收发机的关键技术。然而，如何联合优化数字与模拟波束成形器以服务多个用户，是一个复杂的优化问题。该问题高度依赖精确的信道信息，而现实中因硬件缺陷导致信道获取困难。本文提出一种端到端联合信道估计与混合波束成形方法，输入为接收导频信号，输出为波束成形器。所提神经网络为全模型驱动，参数极少，具备轻量与可解释性。信道估计采用可展开匹配追踪算法，考虑天线系统不完全已知；波束成形则通过可展开投影梯度上升实现。在真实合成信道上的实验表明该方法具有显著潜力。
原文摘要 · Abstract (English)
Hybrid precoding is a key ingredient of cost-effective massive multiple-input multiple-output transceivers. However, setting jointly digital and analog precoders to optimally serve multiple users is a difficult optimization problem. Moreover, it relies heavily on precise knowledge of the channels, which is difficult to obtain, especially when considering realistic systems comprising hardware impairments. In this paper, a joint channel estimation and hybrid precoding method is proposed, which consists in an end-to-end architecture taking received pilots as inputs and outputting pre-coders. The resulting neural network is fully model-based, making it lightweight and interpretable with very few learnable parameters. The channel estimation step is performed using the unfolded matching pursuit algorithm, accounting for imperfect knowledge of the antenna system, while the precoding step is done via unfolded projected gradient ascent. The great potential of the proposed method is empirically demonstrated on realistic synthetic channels.
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