Learning the Wireless V2I Channels Using Deep Neural Networks

TL;DR

This paper introduces a deep learning-based method for real-time channel estimation in high-mobility V2I wireless communications, improving channel prediction accuracy and system performance.

Contribution

It develops a neural network approach that learns and predicts V2I channel responses, addressing challenges of real-time estimation in high-mobility scenarios.

Findings

Neural networks can effectively learn V2I channel properties.

The method improves channel prediction accuracy.

System performance is enhanced using predicted channels.

Abstract

For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance.