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.
Abstract (translated)
车载通信系统面临着由于车辆高速移动和快速变化的环境所带来的显著挑战，这些因素影响了信号传输所依赖的信道。为解决这些问题，基于神经网络（NN）的信道估计方法已被提出。这类方法主要是在高信噪比（SNR）条件下训练的，并假设在噪声较少的情况下训练神经网络可以实现更好的泛化能力。本研究探讨了使用混合SNR数据集来训练基于神经网络的信道估计算法的有效性，而非仅在高SNR数据集上进行训练，后者是许多相关工作中的常见做法。 本文中评估的估计器包括一个采用卷积层和自注意机制的架构；一种利用时间卷积网络和数据导频辅助估计的方法；两种结合经典方法与多层感知机的方法；以及目前最先进的将长短期记忆（LSTM）网络与数据导频辅助和时间平均方法作为后处理手段的模型。 我们的结果显示，仅使用高SNR数据进行训练并不总是最优选择，并且在训练数据集中SNR范围应该被视为可以调整以获得更好性能的一个超参数。这一点通过一些模型在低SNR条件下使用混合SNR数据集进行训练时表现优于单纯使用高SNR数据集训练得到的验证，体现了其有效性。
URL
https://arxiv.org/abs/2502.06824
