Neural Network-based Vehicular Channel Estimation Performance: Effect of Noise in the Training Set

TL;DR

This paper investigates how training neural network-based vehicular channel estimators on mixed SNR datasets improves performance in varying conditions, challenging the assumption that high SNR training alone suffices for good generalization.

Contribution

It demonstrates that including a range of SNR levels in training data enhances neural network channel estimation, highlighting the SNR range as a tunable hyperparameter.

Findings

Mixed SNR training improves low SNR performance

High SNR-only training is not always optimal

SNR range in training data affects estimator robustness

Abstract

Vehicular communication systems face significant challenges due to high mobility and rapidly changing environments, which affect the channel over which the signals travel. To address these challenges, neural network (NN)-based channel estimation methods have been suggested. These methods are primarily trained on high signal-to-noise ratio (SNR) with the assumption that training a NN in less noisy conditions can result in good generalisation. This study examines the effectiveness of training NN-based channel estimators on mixed SNR datasets compared to training solely on high SNR datasets, as seen in several related works. Estimators evaluated in this work include an architecture that uses convolutional layers and self-attention mechanisms; a method that employs temporal convolutional networks and data pilot-aided estimation; two methods that combine classical methods with multilayer perceptrons; and the current state-of-the-art model that combines Long-Short-Term Memory networks with data pilot-aided and temporal averaging methods as post processing. Our results indicate that using only high SNR data for training is not always optimal, and the SNR range in the training dataset should be treated as a hyperparameter that can be adjusted for better performance. This is illustrated by the better performance of some models in low SNR conditions when trained on the mixed SNR dataset, as opposed to when trained exclusively on high SNR data.