Abstract
This paper deals with applying stacking dilated convolutional autoencoder beamforming for Terahertz wave Vehicular ad-hoc networks. A novel solution is proposed to determine the vehicle’s position by big data techniques and deep learning algorithms. Novelty in the solution is obtained via stacking dilated convolutional autoencoder and it is used to train the vehicle traffic datasets.The vehicles direction information is stored in the database for beam alignment. Beam alignment process is used to find the location of vehicles from the database. Multi resolution codebook based beamforming is introduced for finding the vehicles direction information using beam alignment. The proposed terahertz band vehicle propagation model is employed in Vehicular ad-hoc network and results are obtained to determine accuracy and loss curve in multiple operating environments. This model is used to find the vehicles location information using stacking dilated convolutional autoencoder and codebook based beam alignment. Using the traffic dataset, the vehicles’ location information is trained using deep learning algorithm. The dataset is useful for finding out the vehicle’s location information using codebook-based beam alignment. In this article, the vehicles misinformation detection algorithm is also proposed and it is analyzed, where the proposed method gives improved performance in comparison to the existing method with random selection thereby contributing to achieve secure transmission. The research results obtained through this novel approach can be used as benchmark for analyzing and developing TeraHertz Vehicular-Adhoc Networks.
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https://ai.stanford.edu/~jkrause/cars/car_dataset.html. The authors declare that data supporting the findings of this study are available within the article.
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Ananthi, G., Sridevi, S. Stacking Dilated Convolutional AutoEncoder Beamforming for THz Wave Vehicular Ad-Hoc Networks. Wireless Pers Commun 126, 2985–3000 (2022). https://doi.org/10.1007/s11277-022-09848-y
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DOI: https://doi.org/10.1007/s11277-022-09848-y