Highly Accurate and Reliable Wireless Network Slicing in 5 th Generation Networks: A Hybrid Deep Learning Approach

Khan, Sulaiman; Khan, Suleman; Ali, Yasir; Khalid, Muhammad; Ullah, Zahid; Mumtaz, Shahid

Abstract

In current era, the next generation networks like 5 th generation (5G) and 6 th generation (6G) networks requires high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key element for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. An overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability. Keywords-Network slicing, 5G network, hybrid deep learning model, machine learning-based reconfigurable wireless network, LSTM 1. Introduction In this modern technological age mobile communication is an important aspect of human lives due to which the communication devices are growing exponentially [1]. These devices require high bandwidth, mobility, low latency, and better quality of service (QoS) to fulfill the needs of future generation communication. Rapid evolution of communication technology from 2G towards 4G and now upcoming 5G and 6G are prominent examples [2]. The future generation communication also require reliability, seamless operations, and reconfiguration management in heterogeneous wireless networks [3]. The service providers are continuously struggling to fulfill the demands of users and provide reliable communication. To achieve these solutions and fulfill requirements of the 5G networks by expanding the LTE networks to provide higher bandwidth, throughput, and better quality of services. The 5G networks will provide a richer mobility experience in terms of its services, reconfiguration, infrastructure, and large range of operations. It will provide various opportunities for mobilizing a number of application areas like seamless mobility, traffic monitory, healthcare services etc. In the specification of third generation partnership project (3GPP), network slicing is followed as one of the essential component of the 5G network [4]. Through network slicing, the operator will be able to improve the QoS

Citation

Khan, S., Khan, S., Ali, Y., Khalid, M., Ullah, Z., & Mumtaz, S. (2022). Highly Accurate and Reliable Wireless Network Slicing in 5 th Generation Networks: A Hybrid Deep Learning Approach. Journal of Network and Systems Management, 30, Article 29. https://doi.org/10.1007/s10922-021-09636-2

• Journal Article Type: Article
• Acceptance Date: Oct 6, 2021
• Online Publication Date: Jan 27, 2022
• Publication Date: 2022
• Deposit Date: Feb 6, 2023
• Publicly Available Date: Feb 9, 2023
• Journal: Journal of Network and Systems Management
• Print ISSN: 1064-7570
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 30
• Article Number: 29
• DOI: https://doi.org/10.1007/s10922-021-09636-2
• Keywords: Network slicing; 5G network; Hybrid deep learning model; Machine learning-based reconfigurable wireless network; LSTM
• Public URL: https://hull-repository.worktribe.com/output/3904676