An LSTM-based network slicing classification future predictive framework for optimized resource allocation in C-V2X

An LSTM-based network slicing classification future predictive framework for optimized resource allocation in C-V2X

With the advent of 5G communication networks, many novel areas of research have emerged and the spectrum of communicating objects has been diversified. Network Function Virtualization (NFV), and Software Defined Networking (SDN), are the two broader areas that are tremendously being explored to opti...

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Bibliographic Details
Main Authors: Abood, Mohammed Salah, Wang, Hua, He, Dongxuan, Fathy, Maha, Rashid, Sami A., Alibakhshikenari, Mohammad, Virdee, Bal Singh, Khan, Salahuddin, Pau, Giovanni, Dayoub, Iyad, Livreri, Patrizia, Elwi, Taha A.
Format: Article
Language:English
Published: IEEE 2023
Subjects:
600 Technology
620 Engineering & allied operations
Online Access:https://repository.londonmet.ac.uk/8881/1/An_LSTM-Based_Network_Slicing_Classification_Future_Predictive_Framework_for_Optimized_Resource_Allocation_in_C-V2X.pdf

Internet

https://repository.londonmet.ac.uk/8881/1/An_LSTM-Based_Network_Slicing_Classification_Future_Predictive_Framework_for_Optimized_Resource_Allocation_in_C-V2X.pdf

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