Channel estimation in vehicle-to-everything (V2X) communications is a challenging issue due to the fast time-varying and non-stationary characteristics of wireless channel. To grasp the complicated variations of channel with limited number of pilots in the IEEE 802.11p systems, data pilot-aided (DPA) channel estimation has been widely studied. However, the error propagation in the DPA procedure, caused by the noise and the channel variation within adjacent symbols, limits the performance seriously. In this letter, we propose a deep learning based channel estimation scheme, which exploits a long short-term memory network followed by a multilayer perceptron network to solve the error propagation issue. Simulation results show that the proposed scheme outperforms currently widely-used DPA schemes for the IEEE 802.11p-based V2X communications.

中文翻译：

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基于深度学习的Vehicle-to-Everything环境下的信道估计

由于无线信道的快速时变和非平稳特性，车辆对一切 (V2X) 通信中的信道估计是一个具有挑战性的问题。为了掌握 IEEE 802.11p 系统中导频数有限的信道的复杂变化，已经广泛研究了数据导频辅助 (DPA) 信道估计。然而，DPA过程中由噪声和相邻符号内的信道变化引起的错误传播严重限制了性能。在这封信中，我们提出了一种基于深度学习的信道估计方案，该方案利用长短期记忆网络和多层感知器网络来解决错误传播问题。仿真结果表明，所提出的方案优于当前广泛使用的基于 IEEE 802.11p 的 V2X 通信的 DPA 方案。