In this paper, we consider the high-speed railway (HSR) communication bases on the long-term evolution for railway (LTE-R) platform. Since the large Doppler spread is introduced due to the movement of the train, the orthogonality of subcarriers is destroyed resulting in the inter-carrier interference (ICI). Moreover, the critical speed of transceivers moving leads to a very fast-time varying channel within an OFDM symbol, hence the existing frequency domain channel estimation (FDCE) method cannot reliably estimate the channel state information (CSI). To better track the fast-time varying channel, we propose a new framework for LTE-R systems. First, we modify the WINNER II channel model and the D2a propagation scenario to approximate the multipath fading and high Doppler spread in high mobility scenarios. Second, we use a novel pilot structure-based time domain channel estimation (TDCE), helping to track the channel variations for each channel path separately. The CSI at data subcarriers is estimated by using different conventional interpolation methods to reconstruct the ICI channel matrix in the frequency domain. Different from the linear channel model, the channel time-variations of current OFDM data symbol are predicted from the third-order polynomial function, which is reconstructed base on CIR at the pilot position by using several constitutive OFDM symbols. The cubic Hermite function is chosen by considering its simplicity and efficiency. Lastly, we propose the deep neural network (DNN) based CE in LTE-R systems, which helps to further improve the accuracy of estimated channel information in the previous state, especially at low signal-to-noise (SNR) level. The simulation results show that our proposed CE method for each channel path agrees well with the theoretical derivation and the simulation. The system performance with the proposed framework is significantly improved in comparison with state-of-the-art methods.

在本文中，我们考虑了基于铁路长期演进（LTE-R）平台的高速铁路（HSR）通信。由于列车的运动会引入大的多普勒扩展，因此会破坏子载波的正交性，从而导致载波间干扰（ICI）。此外，收发器移动的临界速度导致OFDM符号内的信道变化非常快，因此现有的频域信道估计（FDCE）方法无法可靠地估计信道状态信息（CSI）。为了更好地跟踪快速时变信道，我们提出了LTE-R系统的新框架。首先，我们修改WINNER II信道模型和D2a传播方案，以近似在高移动性方案中的多径衰落和高多普勒扩展。第二，我们使用一种新颖的基于导频结构的时域信道估计（TDCE），有助于分别跟踪每个信道路径的信道变化。通过使用不同的常规插值方法在频域中重建ICI信道矩阵，可以估算数据子载波处的CSI。与线性信道模型不同，当前OFDM数据符号的信道时变是根据三阶多项式函数预测的，该函数是通过使用多个本构OFDM符号在导频位置基于CIR重建的。三次Hermite函数是通过考虑其简单性和效率来选择的。最后，我们在LTE-R系统中提出了基于深度神经网络（CE）的CE，这有助于进一步提高先前状态下估算的信道信息的准确性，特别是在低信噪比（SNR）的情况下。仿真结果表明，我们针对每个通道路径提出的CE方法与理论推导和仿真结果吻合良好。与最先进的方法相比，所提出的框架的系统性能得到了显着改善。