论文标题
MPNET：可变深度展开的神经网络，用于大规模MIMO通道估计
mpNet: variable depth unfolded neural network for massive MIMO channel estimation
论文作者
论文摘要
大量多输入多输出（MIMO）通信系统在数据速率和能源效率方面都具有巨大的潜力，尽管对于大量天线而言，渠道估计变得具有挑战性。使用物理模型可以通过基于繁殖物理的先验信息来减轻问题。但是，这样的模型基于简化的假设，并且需要精确了解系统的配置，该系统在实践中是不现实的。在本文中，我们介绍了MPNET，这是一个专门设计用于大规模MIMO通道估计的不展开的神经网络。它以无监督的方式在线培训。此外，MPNET在计算上是有效的，并自动将其深度调整为信噪比（SNR）。我们提出的方法通过允许基于传入数据自动纠正其通道估计算法的物理通道模型的灵活性，而无需单独的离线训练阶段，它应用于现实的毫米波波通道并显示出巨大的性能，表现出巨大的估计误差，几乎可以通过一个完美校准的系统来实现较低的误差。它还允许事件检测和自动校正，使BS弹性并能够自动适应其环境变化。
Massive multiple-input multiple-output (MIMO) communication systems have a huge potential both in terms of data rate and energy efficiency, although channel estimation becomes challenging for a large number of antennas. Using a physical model allows to ease the problem by injecting a priori information based on the physics of propagation. However, such a model rests on simplifying assumptions and requires to know precisely the configuration of the system, which is unrealistic in practice.In this paper we present mpNet, an unfolded neural network specifically designed for massive MIMO channel estimation. It is trained online in an unsupervised way. Moreover, mpNet is computationally efficient and automatically adapts its depth to the signal-to-noise ratio (SNR). The method we propose adds flexibility to physical channel models by allowing a base station (BS) 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 millimeter wave channels and shows great performance, achieving a channel estimation error almost as low as one would get with a perfectly calibrated system. It also allows incident detection and automatic correction, making the BS resilient and able to automatically adapt to changes in its environment.
