Deep MIMO Detection Scheme for High-Speed Railways with Wireless Big Data

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.