IEEE 802.11p standard defines wireless technology protocols that enable vehicular transportation and manage traffic efficiency. A major challenge in the development of this technology is ensuring communication reliability in highly dynamic vehicular environments, where the wireless communication channels are doubly selective, thus making channel estimation and tracking a relevant problem to investigate. In this paper, a novel deep learning (DL)-based weighted interpolation estimator is proposed to accurately estimate vehicular channels especially in high mobility scenarios. The proposed estimator is based on modifying the pilot allocation of the IEEE 802.11p standard so that more transmission data rates are achieved. Extensive numerical experiments demonstrate that the developed estimator significantly outperforms the recently proposed DL-based frame-by-frame estimators in different vehicular scenarios, while substantially reducing the overall computational complexity.

翻译：IEE 802.11p 标准定义了无线技术协议,使车辆运输得以进行,并管理交通效率。开发这一技术的一个主要挑战是确保高度动态车辆环境中的通信可靠性,因为无线通信频道具有双重选择性,从而使频道估计和跟踪成为需要调查的一个相关问题。在本文中,提议了一个新的深层次学习(DL)基于加权的内插估计器,以准确估计车辆通道,特别是在高流动性情况下。拟议的估计器基于修改IEE 802.11p 标准的试点分配,从而实现更多的传输数据率。广泛的数字实验表明,开发的测算器明显超过最近提议的基于DL的框架测算器,同时大幅度降低了不同车辆的计算复杂性。