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面向发展中的商业需求和网络挑战的网络切片综合分析。

Comprehensive Analysis of Network Slicing for the Developing Commercial Needs and Networking Challenges.

机构信息

Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan.

Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia.

出版信息

Sensors (Basel). 2022 Sep 1;22(17):6623. doi: 10.3390/s22176623.

DOI:10.3390/s22176623
PMID:36081079
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9459685/
Abstract

Network slicing (NS) is one of the most prominent next-generation wireless cellular technology use cases, promising to unlock the core benefits of 5G network architecture by allowing communication service providers (CSPs) and operators to construct scalable and customized logical networks. This, in turn, enables telcos to reach the full potential of their infrastructure by offering customers tailored networking solutions that meet their specific needs, which is critical in an era where no two businesses have the same requirements. This article presents a commercial overview of NS, as well as the need for a slicing automation and orchestration framework. Furthermore, it will address the current NS project objectives along with the complex functional execution of NS code flow. A summary of activities in important standards development groups and industrial forums relevant to artificial intelligence (AI) and machine learning (ML) is also provided. Finally, we identify various open research problems and potential answers to provide future guidance.

摘要

网络切片(NS)是下一代无线蜂窝技术的最突出用例之一,有望通过允许通信服务提供商(CSP)和运营商构建可扩展和定制的逻辑网络,来释放 5G 网络架构的核心优势。这反过来又使电信公司能够通过提供满足客户特定需求的定制网络解决方案来充分利用其基础设施的潜力,在没有两个业务具有相同要求的时代,这一点至关重要。本文介绍了 NS 的商业概述,以及对切片自动化和编排框架的需求。此外,它将解决当前的 NS 项目目标以及 NS 代码流的复杂功能执行。还提供了与人工智能(AI)和机器学习(ML)相关的重要标准开发组和工业论坛的活动摘要。最后,我们确定了各种开放的研究问题和潜在的答案,以提供未来的指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae3/9459685/2366d84d7063/sensors-22-06623-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae3/9459685/4fbc7fe8cc80/sensors-22-06623-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae3/9459685/693f257fae35/sensors-22-06623-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae3/9459685/3981ff613183/sensors-22-06623-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae3/9459685/7d3c8abf637c/sensors-22-06623-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae3/9459685/2366d84d7063/sensors-22-06623-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae3/9459685/4fbc7fe8cc80/sensors-22-06623-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae3/9459685/0d16d1952cf7/sensors-22-06623-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae3/9459685/831280576929/sensors-22-06623-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae3/9459685/d40adc771a63/sensors-22-06623-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae3/9459685/693f257fae35/sensors-22-06623-g005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae3/9459685/2366d84d7063/sensors-22-06623-g008.jpg

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