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

Simbarashe Aldrin Ngorima, Albert Helberg, Marelie H. Davel

TL;DR

The paper tackles NN-based channel estimation for high-mobility vehicular channels and questions the common practice of training only on high-SNR data. It evaluates multiple architectures (CNN-Transformer, TCN-DPA, STA-MLP, TRFI-MLP, LSTM-DPA-TA) trained on mixed-SNR versus high-SNR datasets using an IEEE 802.11p–style channel model. Key findings show mixed-SNR training improves generalization in low-SNR conditions for several models (notably CNN-Transformer, DPA-TCN, and TRFI-MLP) and can outperform the state-of-the-art in some regimes, while others (such as LSTM-DPA-TA and STA-MLP) may benefit more from high-SNR training. The work highlights the SNR range as a crucial hyperparameter for training NN-based estimators and provides practical guidance for developing robust vehicular channel estimation systems.

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.

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

TL;DR

The paper tackles NN-based channel estimation for high-mobility vehicular channels and questions the common practice of training only on high-SNR data. It evaluates multiple architectures (CNN-Transformer, TCN-DPA, STA-MLP, TRFI-MLP, LSTM-DPA-TA) trained on mixed-SNR versus high-SNR datasets using an IEEE 802.11p–style channel model. Key findings show mixed-SNR training improves generalization in low-SNR conditions for several models (notably CNN-Transformer, DPA-TCN, and TRFI-MLP) and can outperform the state-of-the-art in some regimes, while others (such as LSTM-DPA-TA and STA-MLP) may benefit more from high-SNR training. The work highlights the SNR range as a crucial hyperparameter for training NN-based estimators and provides practical guidance for developing robust vehicular channel estimation systems.

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.

Paper Structure

This paper contains 22 sections, 8 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. System Model
  4. Well-known Vehicular Channel Estimation Schemes
  5. DPA Estimation:
  6. STA Estimation Scheme:
  7. TRFI Estimation Scheme:
  8. MLP-based Estimators
  9. TCN-DPA Estimator
  10. LSTM-DPA-TA Estimator
  11. CNN-Transformer
  12. Methodology
  13. Data Preparation
  14. Mixed SNR Dataset
  15. High SNR Dataset
  16. ...and 7 more sections

Figures (4)

  • Figure 1: The IEEE 802.11p frame structure, illustrating the allocation of subcarriers for pilot and data transmission.
  • Figure 2: Transformed IEEE 802.11p frame structure used as input to deep learning models, composed of 100 interleaved complex symbols and 52 subcarriers.
  • Figure 3: Comparison of BER performance for various channel estimators trained on high SNR and mixed SNR datasets.
  • Figure 4: Difference in BER between models trained on mixed SNR and high SNR datasets.