Survey on Machine Learning for Intelligent End-to-End Communication Toward 6G: From Network Access, Routing to Traffic Control and Streaming Adaption

The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite important for network optimization. The current 5G and conceived 6G network in the future with ultra high density, bandwidth, mobility and large scale brings urgent requirement of high efficient end-to-end optimization methods. The conventional network optimization methods without learning and intelligent decision ability are hard to handle the high complexity and dynamic scenarios of 6G. Recently, machine learning based QoS and QoE aware network optimization algorithms emerge as a hot research area and attract much attention, which is widely acknowledged as the potential solution for end-to-end optimization in 6G. However, there are still many critical issues of employing machine learning in networks, especially in 6G. In this paper, we give a comprehensive survey on the recent machine learning based network optimization methods to guarantee the end-to-end QoS and QoE. To easy to follow, we introduce the investigated works following the end-to-end transmission flow from network access, routing to network congestion control and adaptive steaming control. Then we discuss some open issues and potential future research directions.

Automated summary

End-to-end quality of service (QoS) and quality of experience (QoE) guarantee is crucial for network optimization, especially in 5G and future 6G networks. Machine learning algorithms are seen as key solutions for optimizing 6G networks, but there are still many challenges and open issues to be addressed.