机器学习:5G复杂性的Panacea

Q3 Decision Sciences Journal of ICT Standardization Pub Date : 2019-01-01 DOI:10.13052/jicts2245-800X.726
N. Hari Kumar;Sandhya Baskaran
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引用次数: 3

摘要

下一代技术的转型给电信生态系统带来了一系列令人兴奋的应用和挑战,进而为新的收入流铺平了道路,这不是一个神话。5G实现了超高的数据速率和极低的延迟,这使电信运营商能够通过增强5G系统的基础设施、软件和硬件组件,促进loT和下一代工业增强等有趣的相似之处,如自动驾驶汽车、联网矿山、联网农业和关键任务通信。随着5G即将推出的新功能,如多输入多输出(MIMO)、网络切片、虚拟网络功能、室内定位、机器对机器(M2M)功能受到高度赞赏,它也带来了一系列新的挑战,如实时动态配置、低延迟切换。这些挑战可以通过将人工智能技术应用于5G系统关键部件来解决。在本文中,我们讨论了一些主要挑战,如数据突发、提高性能、5G系统附加新组件的容错和流量管理、对现有技术的必要升级,以及机器学习(ML)和人工智能(AI)如何成为这些绊脚石的不言自明的答案。
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Machine Learning: The Panacea for 5G Complexities
It's not a myth that transition in next generation technology brings with it a set of exciting applications as well as challenges to the telecom ecosystem and in-turn paves way for new revenue streams. 5G enables ultra-high data rates, exceptional low latencies which enables the telecom operator for the facilitation of interesting parallels like loT and Next-Gen Industrial enhance-ments like autonomous vehicles, connected mines, connected agriculture and mission critical communications by enhancing infrastructure, software and hardware components of the 5G system. As imminent new features of 5G like Multiple Input Multiple Output (MIMO), network slices, virtual network functions, indoor localization, Machine to Machine (M2M) capabilities are highly appreciated, it also opens new set of challenges like real time dynamic configurations, low latency handovers. These challenges can be addressed with the application of AI technologies to components at the crux of 5G system. Here in this paper, we discuss some of the major challenges such as data burst, improving performance, fault tolerance and traffic management with new components appended to the 5G system, required upgrades to existing technology and how Machine Learning (ML), Artificial Intelligence (AI), becomes the self-evident answer to these stumbling blocks.
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来源期刊
Journal of ICT Standardization
Journal of ICT Standardization Computer Science-Information Systems
CiteScore
2.20
自引率
0.00%
发文量
18
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