论文标题
部分可观测时空混沌系统的无模型预测
Online unsupervised deep unfolding for MIMO channel estimation
论文作者
论文摘要
通道估计是MIMO系统中的一个困难问题。使用物理模型可以缓解问题，并根据传播的物理学注入先验信息。但是，这样的模型基于简化假设，并需要精确了解系统配置，这是不现实的。在本文中，我们建议在大规模的MIMO环境中进行频道估计，从而通过展现频道估计算法（匹配的追踪）作为神经网络，从而为物理模型增加了灵活性。这导致了一个计算高效的神经网络，该神经网络可以在以不完善的模型初始化时在线训练。该方法允许基站基于传入数据自动纠正其通道估计算法，而无需单独的离线训练阶段。它应用于逼真的通道并显示出良好的性能，达到通道估计误差几乎与完美校准的系统一样低。
Channel estimation is a difficult problem in MIMO systems. Using a physical model allows to ease the problem, injecting a priori information based on the physics of propagation. However, such models rest on simplifying assumptions and require to know precisely the system configuration, which is unrealistic.In this paper, we propose to perform online learning for channel estimation in a massive MIMO context, adding flexibility to physical models by unfolding a channel estimation algorithm (matching pursuit) as a neural network. This leads to a computationally efficient neural network that can be trained online when initialized with an imperfect model. The method allows a base station to automatically correct its channel estimation algorithm based on incoming data, without the need for a separate offline training phase.It is applied to realistic channels and shows great performance, achieving channel estimation error almost as low as one would get with a perfectly calibrated system.
