Machine learning: The Panacea for 5G complexities

被引:0
作者
Hari Kumar N. [1 ]
Baskaran S. [1 ]
机构
[1] Ericsson Research, India
来源
Journal of ICT Standardization | 2019年 / 7卷 / 02期
关键词
4G; 5G; Access independent; Artificial intelligence; Automation; Datacentre; Deep learning; Information communication technologies (ICT); Machine learning; Management; ML; Multi-access; Open source; Virtualization;
D O I
10.13052/jicts2245-800X.726
中图分类号
学科分类号
摘要
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 IoT and Next-Gen Industrial enhancements 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. © 2019 the Author(s). All rights reserved.
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页码:157 / 170
页数:13
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