论文标题
具有少量ADC的MIMO系统的神经网络优化的通道估计器和训练信号设计
Neural Network-Optimized Channel Estimator and Training Signal Design for MIMO Systems with Few-Bit ADCs
论文作者
论文摘要
本文涉及具有少量ADC的MIMO系统中的通道估计。在这些系统中，以封闭形式获得的线性最小于点误差（MMSE）通道估计器不是最佳解决方案。我们首先考虑一个深神经网络（DNN），并将其作为少量MIMO系统的非线性MMSE通道估计器进行训练。然后，我们首次尝试使用DNN同时优化训练信号和MMSE通道估计器。具体来说，我们提出了一个具有专门第一层的自动编码器，其权重嵌入了训练信号矩阵。因此，训练有素的自动编码器提示了一种新的训练信号设计，该设计是针对正在考虑的MIMO频道模型定制的。
This paper is concerned with channel estimation in MIMO systems with few-bit ADCs. In these systems, a linear minimum mean-squared error (MMSE) channel estimator obtained in closed-form is not an optimal solution. We first consider a deep neural network (DNN) and train it as a non-linear MMSE channel estimator for few-bit MIMO systems. We then present a first attempt to use DNN in optimizing the training signal and the MMSE channel estimator concurrently. Specifically, we propose an autoencoder with a specialized first layer, whose weights embed the training signal matrix. Consequently, the trained autoencoder prompts a new training signal design that is customized for the MIMO channel model under consideration.
