论文标题
对6G无线网络的机器学习算法的调查
A Survey of Machine Learning Algorithms for 6G Wireless Networks
论文作者
论文摘要
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流。通过深度学习AI技术代替传统算法已经大大降低了功耗并改善了系统性能。此外，ML算法的实施还使无线网络服务提供商（i）提供了适用于网络边缘的分布式AI/ML架构的高自动化级别，（ii）在整个访问网络上实现基于应用程序的流量转向，（iii）启用动态网络，以解决各种服务的各种服务范围，以解决各种服务范围的各种服务范围，并启用了6g semporty emply（iiv和IIV）。 在本章中，我们审查/调查适用于6G无线网络的ML技术。并列出需要及时解决方案的研究的开放问题。
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.
