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.

中文翻译：

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特邀社论：5G 及其他网络中网络切片的智能、优化和可解释编排

网络切片是第五代 (5G) 及以后 (B5G) 网络中讨论最多的话题。网络切片功能将 5G 和 B5G 网络与前几代网络区分开来，因为它用端到端多服务供应和多租户取代了传统的服务质量 (QoS) 概念。需要在网络切片中智能地分配用于计算、网络、存储和电源的各种资源。传统的优化/资源调度技术通常是一维的，在大规模 5G/B5G 网络中可能无法很好地扩展。因此，迫切需要智能地解决网络切片的编排和管理问题。由于 5G 之后的网络将大量使用嵌入式智能，如何利用基于人工智能的技术，例如机器学习、深度学习和强化学习，解决和解决各种复杂的网络切片问题正在成为一个具有挑战性的问题。客座编辑努力与学术界和工业界的研究人员联系，以在本期特刊中解决这些问题，以寻找一种既智能又实用的真正智能的 B5G 网络部署。