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arXiv 2203.08429cs.NI
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6G无线网络机器学习算法综述

A Survey of Machine Learning Algorithms for 6G Wireless Networks

Anita Patil, Sridhar Iyer, Rahul Jashvantbhai Pandya

摘要:

无线技术中人工智能/机器学习(AI/ML)集成的主要重点是减少资本支出、优化网络性能并建立新的收入来源。用深度学习人工智能技术取代传统算法,大大降低了功耗,提高了系统性能。此外,ML算法的实现还使得无线网络服务提供商能够(i)从可应用于网络边缘的分布式AI/ML架构提供高自动化水平,(ii)在接入网络上实现基于应用的业务导向,(iii)实现动态网络切片以解决具有变化的服务质量要求的不同场景,以及(iv)实现跨各种6G通信平台的无处不在的连接。在本章中,我们回顾/调查ML技术,适用于6G无线网络。并列出需要及时解决的研究中的公开问题。

英文摘要:

The primary focus of Artificial Intelligence/Machine Learning (AI/ML) integration within the wireless technology is to reduce capital expenditures, optimize network performance, and build new revenue streams. Replacing traditional algorithms with deep learning AI techniques have dramatically reduced the power consumption and improved the system performance. Further, implementation of ML algorithms also enables the wireless network service providers to (i) offer high automation levels from distributed AI/ML architectures applicable at the network edge, (ii) implement application-based traffic steering across the access networks, (iii) enable dynamic network slicing for addressing different scenarios with varying quality of service requirements, and (iv) enable ubiquitous connectivity across the various 6G communication platforms. In this chapter, we review/survey the ML techniques which are applicable to the 6G wireless networks. and also list the open problems of research which require timely solutions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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