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    Guest Editorial: Smart, optimal, and explainable orchestration of network slices in 5G and beyond networks

    Fadlullah, ZM, Fouda, MM, Rabie, K ORCID logoORCID: https://orcid.org/0000-0002-9784-3703 and Li, R (2022) Guest Editorial: Smart, optimal, and explainable orchestration of network slices in 5G and beyond networks. IEEE Wireless Communications, 29 (1). pp. 94-95. ISSN 1536-1284

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    Abstract

    Network slicing is a much discussed topic in fifth generation (5G) and beyond (B5G) networks. The network slice feature differentiates 5G and B5G networks from the earlier generations since it replaces the conventional concept of quality of service (QoS) with end-to-end multi-service provisioning and multi-tenancy. A diverse set of resources for computing, networking, storage, and power need to be smartly assigned in network slices. Traditional optimization/resource scheduling techniques are typically one-dimensional and may not scale well in large-scale 5G/B5G networks. Therefore, there is a pressing need to smartly address the orchestration and management of network slices. Since beyond 5G networks will heavily use embedded intelligence, how to leverage AI-based techniques, such as machine learning, deep learning, and reinforcement learning, to address and solve the various complex network slicing problems is emerging as a challenging problem. The Guest Editors worked hard to reach out to researchers from academia and industry to address these points in this Special Issue in search of a genuinely intelligent B5G network rollout that could be both smart and practical.

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