CNN aided Weighted Interpolation for Channel Estimation in Vehicular Communications

TL;DR

This paper introduces a deep learning-based weighted interpolation method for vehicular channel estimation that improves accuracy and reduces complexity in high mobility scenarios, enhancing IEEE 802.11p communication reliability.

Contribution

It presents a novel DL-based weighted interpolation estimator that modifies pilot allocation in IEEE 802.11p to improve channel estimation in vehicular environments.

Findings

Significantly outperforms existing DL-based estimators.

Achieves higher data transmission rates.

Reduces computational complexity.

Abstract

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.