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

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 and Elwi, Taha A. (2023) An LSTM-based network slicing classification future predictive framework for optimized resource allocation in C-V2X. IEEE Access, 11. pp. 129300-12910. ISSN 2169-3536

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Official URL: https://ieeexplore.ieee.org/document/10315120

Abstract / Description

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 optimize the network performance parameters. Cellular Vehicle-to-Everything (C-V2X) is one such example of where end-to-end communication is developed with the aid of intervening network slices. Adoption of these technologies enables a shift towards Ultra-Reliable Low-Latency Communication (URLLC) across various domains including autonomous vehicles that demand a hundred percent Quality of Service (QoS) and extremely low latency rates. Due to the limitation of resources to ensure such communication requirements, telecom operators are profoundly researching software solutions for network resource allocation optimally. The concept of Network Slicing (NS) emerged from such end-to-end network resource allocation where connecting devices are routed toward the suitable resources to meet their requirements. Nevertheless, the bias, in terms of finding the best slice, observed in the network slices renders a non-optimal distribution of resources. To cater to such issues, a Deep Learning approach has been developed in this paper. The incoming traffic has been allocated network slices based on data-driven decisions as well as predictive network analysis for the future. A Long Short Term Memory (LSTM) time series prediction approach has been adopted that renders optimal resource utilization, lower latency rates, and high reliability across the network. The model will further ensure packet prioritization and will retain resource margin for crucial ones.

Item Type: Article
Uncontrolled Keywords: cellular vehicles to everything (C-V2X); deep learning; latency; long short term memory (LSTM); machine learning; network slicing; optimization; reliability
Subjects: 600 Technology
600 Technology > 620 Engineering & allied operations
Department: School of Computing and Digital Media
Depositing User: Bal Virdee
Date Deposited: 27 Nov 2023 09:41
Last Modified: 20 Dec 2023 16:58
URI: https://repository.londonmet.ac.uk/id/eprint/8881

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