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首页> 外文会议>2019 IEEE 89th Vehicular Technology Conference >Deep MIMO Detection Scheme for High-Speed Railways with Wireless Big Data
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Deep MIMO Detection Scheme for High-Speed Railways with Wireless Big Data

机译：具有无线大数据的高速铁路的Deep MIMO检测方案

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摘要

With the certainty of the high-speed railway(HSR) route, high-speed train(HST) is always driving periodically, and it is quite meaningful to assist HSR wireless signal detection through historical big data. One key challenge in this detection is that the HSR wireless channel is varying when the HST drives to different places, thus the data under various channel environment needs to be analyzed separately. In this paper, we propose a deep learning algorithm to detect the multiple input multiple output(MIMO) signal for HSR scenarios, and the entire algorithm framework is divided into two phases: offline training phase and online detection phase. At the offline training phase, we first analyze the data of HSR at each location, and explore a division scheme to further divide each scene into multiple smaller regions so that data in each divided region can share the same network. Then, the deep neural network(DNN) is constructed and trained for each divided region. At the online detection phase, the HST locates the current region according to the location information achieved by GPS, and selects the corresponding DNN model to detect the signal in real time. In addition, this DNN structure combines channel estimation and signal detection. Thus, the HSR detection system can detect the MIMO signal directly without the step of channel estimation. Finally, the simulation results show that the deep learning detection algorithm has better accuracy than those traditional detection algorithms, such as the least square(LS) algorithm and the minimum mean-square error(MMSE) algorithm.
机译：有了高速铁路（HSR）路线的确定性，高速列车（HST）总是定期行驶，通过历史大数据协助HSR无线信号检测非常有意义。这种检测的一个主要挑战是，当HST行驶到不同位置时，HSR无线信道会发生变化，因此需要分别分析各种信道环境下的数据。在本文中，我们提出了一种深度学习算法来检测HSR场景中的多输入多输出（MIMO）信号，并且整个算法框架分为两个阶段：离线训练阶段和在线检测阶段。在离线训练阶段，我们首先分析每个位置的高铁数据，并探索一种划分方案，将每个场景进一步划分为多个较小的区域，以便每个划分区域中的数据可以共享同一网络。然后，针对每个划分的区域构造和训练深度神经网络。在在线检测阶段，HST根据GPS所获得的位置信息来定位当前区域，并选择相应的DNN模型来实时检测信号。另外，这种DNN结构将信道估计和信号检测结合在一起。因此，HSR检测系统可以直接检测MIMO信号，而无需信道估计步骤。仿真结果表明，与最小二乘算法，最小均方误差算法等传统检测算法相比，深度学习检测算法具有更高的精度。

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