论文标题
数据驱动的深度学习以设计大量MIMO的飞行员和渠道估计器
Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO
论文作者
论文摘要
在本文中，我们提出了一种数据驱动的深度学习方法（DL），以共同设计宽带多输入多输出（MIMO）系统的飞行员信号和通道估计器。通过利用大量MIMO通道的角域可压缩性，构想的DL框架可以从不确定的测量值中可靠地重建高维通道。具体而言，我们设计了一个由降维网络和重建网络组成的端到端深神经网络（DNN）体系结构，以分别模仿飞行员信号和渠道估计器，可以通过数据驱动的深度学习来获取。对于降低降低网络，我们通过压缩高维大型MIMO通道向量作为低维接收的测量值的输入来设计完全连接的层，其中权重被视为飞行员信号。对于重建网络，我们设计了一个完全连接的层，然后是多个级联卷积层，该层将重建高维通道作为输出。通过定义输入和输出之间的均方误差为损耗函数，我们利用ADAM算法来训练上述广泛的通道样本的端到端DNN。这样，可以同时获得试点信号和通道估计器。仿真结果表明，所提出的解决方案优于最先进的压缩传感方法。
In this paper, we propose a data-driven deep learning (DL) approach to jointly design the pilot signals and channel estimator for wideband massive multiple-input multiple-output (MIMO) systems. By exploiting the angular-domain compressibility of massive MIMO channels, the conceived DL framework can reliably reconstruct the high-dimensional channels from the under-determined measurements. Specifically, we design an end-to-end deep neural network (DNN) architecture composed of dimensionality reduction network and reconstruction network to respectively mimic the pilot signals and channel estimator, which can be acquired by data-driven deep learning. For the dimensionality reduction network, we design a fully-connected layer by compressing the high-dimensional massive MIMO channel vector as input to low-dimensional received measurements, where the weights are regarded as the pilot signals. For the reconstruction network, we design a fully-connected layer followed by multiple cascaded convolutional layers, which will reconstruct the high-dimensional channel as the output. By defining the mean square error between input and output as loss function, we leverage Adam algorithm to train the end-to-end DNN aforementioned with extensive channel samples. In this way, both the pilot signals and channel estimator can be simultaneously obtained. The simulation results demonstrate that the superiority of the proposed solution over state-of-the-art compressive sensing approaches.
