A Data Pilot-Aided Temporal Convolutional Network for Channel Estimation in IEEE 802.11p Vehicle-to-Vehicle Communications

TL;DR

This paper proposes a novel data pilot-aided temporal convolutional network approach with temporal averaging for improved channel estimation in vehicle-to-vehicle communications, significantly enhancing BER performance under mobility conditions.

Contribution

It introduces a new TCN-based channel estimator with data pilot assistance and temporal averaging, outperforming classical methods in VTV-SDWW channels.

Findings

BER improved by up to 1 order of magnitude with TCN-DPA-TA.

BER improved by up to 0.7 magnitude with TCN-DPA without TA.

Demonstrates effectiveness of deep learning in dynamic channel estimation.

Abstract

In modern communication systems, having an accurate channel estimator is crucial. However, when there is mobility, it becomes difficult to estimate the channel and the pilot signals, which are used for channel estimation, become insufficient. In this paper, we introduce the use of Temporal Convolutional Networks (TCNs) with data pilot-aided (DPA) channel estimation and temporal averaging (TA) to estimate vehicle-to-vehicle same direction with Wall (VTV-SDWW) channels. The TCN-DPA-TA estimator showed an improvement in Bit Error Rate (BER) performance of up to 1 order of magnitude. Furthermore, the BER performance of the TCN-DPA without TA also improved by up to 0.7 magnitude compared to the best classical estimator.