Systematic Review of Network Slicing Resource Management in 5G

This chapter provides a comprehensive analysis of the management of network slicing resources in 5G networks, with a specific emphasis on scholarly works published from 2019 onward. The implementation of network slicing plays a crucial role in addressing the varied service requirements of 5G, encompassing improved mobile broadband, highly reliable low-latency communication, and extensive machine-type communication. The classification of papers in the review encompasses four distinct categories, namely end-to-end (E2E), core, radio access network (RAN), and miscellaneous. End-to-end (E2E) research endeavors to investigate novel methodologies such as Deep Q-Networks (DQN) and real-time data-driven strategies in order to enhance resource allocation efficiency and mitigate latency. Core-focused papers utilize a combination of Lagrangian relaxation and deep reinforcement learning techniques to optimize resource allocation and maximize revenue generation. Subsequent investigations should aim to evaluate the efficacy of these models on more extensive networks, utilize sophisticated machine learning methodologies, and investigate the potential for resource allocation in multitenant environments. This review aims to provide an extensive reference for researchers by examining the present trends and research gaps in resource management for 5G network slicing.