Machine Learning Based mmWave Orchestration for Edge Gaming QoE Enhancement

被引：2

作者：

Luo, Hao ^{[1
]}

Wei, Hung-Yu ^{[1
]}

机构：

[1] Natl Taiwan Univ, Dept Elect Engn, Taipei, Taiwan

来源：

2021 IEEE 94TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-FALL) | 2021年

关键词：

mmWave communication; network management; machine learning; edge computing; resource allocation; EXPERIENCE; FRAMEWORK;

D O I：

10.1109/VTC2021-FALL52928.2021.9625307

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Millimeter wave (mmWave) is a crucial component in 5G and beyond 5G communications. However, the dense deployment of mmWave transceivers imposes a heavy burden on the management of radio access network (RAN). This challenge increases the need for autonomous network management methods leveraging machine learning (ML) techniques. In particular, mmWave beam selection is a critical issue for the management of RAN due to the large training overhead on mmWave transceivers. To this end, a new beam tracking method based on sequence-tosequence (Seq2Seq) learning is proposed. Besides, thanks to edge computing technologies, network management algorithms and delay-sensitive user applications can be hosted on edge servers in close proximity. Due to limited resources on the edge server, the resource allocation problem for beam tracking and edge gaming is investigated with the aim of maximizing game quality of experience (QoE). Simulation results verify the effectiveness of the proposed orchestration scheme.

引用

页数：6

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