Citation

Ahmed, Tahir H. and Tiang, Jun Jiat and Mahmud, Azwan and Chung, Gwo Chin and Do, Dinh Thuan (2023) Proposed CtCNet-HDRNN: A Cornerstone in the Integration of 5G mmWave and DSRC for High-Speed Vehicular Networks. IEEE Access, 11. pp. 126482-126506. ISSN 2169-3536

Text 6.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

This study proposes an innovative integration of the Car-to-Car Network-Hierarchical deep neural network (CtCNET-HDRNN) model with Fifth generation (5G) and Dedicated Short-Range Communications (DSRC) systems, streamlining computational efficiency in edge computing. CtCNET-HDRNN is a specialized deep learning model designed for vehicular communication, allowing vehicles to exchange information seamlessly in a connected environment. It harnesses an adaptive learning rate and regularization within the model’s advanced training methodology, ensuring optimal data fit, superior generalization, and efficient convergence. A key novelty lies in the introduction of a Sparse Deep Recurrent Neural Network (SDRNN), which significantly reduces computational complexity by pruning insignificant connections, making it suitable for deployment on resource-constrained edge devices. SDRNN is a variant of recurrent neural networks designed to minimize computational burden while maintaining high performance in time-series data analysis. Furthermore, this research presents an original integration model, adeptly merging the CtCNET-HDRNN model with the Millimeter wave (mmWave) of 5G and Monte Carlo for DSRC systems for seamless data transmission. The mmWave technology offers high-speed communication capabilities, while Monte Carlo enables adaptive collision avoidance and efficient channel access control for vehicular networks. Beyond immediate computational gains, this integrated model also contributes significantly to edge computing research and practical applications, promising enhanced system performance and improved user experience in vehicular communication scenarios. The proposed approach opens new possibilities for efficient and reliable communication in connected vehicles, laying the foundation for safer and smarter transportation systems.

Item Type:	Article
Uncontrolled Keywords:	5G, DSRC, V2V, AI/ML, deep learning, AIoT, deep reinforcement learning, RNN, VANET.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	07 Dec 2023 02:24
Last Modified:	07 Dec 2023 02:24
URII:	http://shdl.mmu.edu.my/id/eprint/11929

Downloads

Edit (login required)