| 摘要(英) |
The traffic flow grows rapidly in recent years. According to Cisco report, the network traffic of 2022 will be more than 8 times of 2017, and the traffic flow generated by image-type application will occupy up to 80% of total traffic flow. In addition the 5G system’s characteristic of flexibility and adjustability let the 5G system allow to serve more different application with different request and characteristic, e.g., The ultra-high video, video streaming and online interactive virtual reality gaming. So that next generation mobile communication system is going to face a higher congestion and higher complexity network environment. Therefore, the resource scheduling problem is more complicate than the past. Many papers devoted to proposing the best resource scheduling scheme. The results show the machine learning are more suitable for the 5G network. There are some papers analyze the network environment to assist the machine learning to make better scheduling decision. In this article, the DDPG, a machine learning algorithm developed by Google DeepMind is used to schedule resources in a multi-base network environment, and allocate resources to all base stations through the edge cloud server. In addition, we have added a grouping mechanism to improved bandwidth usage efficiency through DIBR and MBMS technologies. From the experimental results, we can see that our machine learning method cannot achieve the highest transmission rate compared with the single arrangement method, but the learning method proposed by the user can achieve the highest satisfaction rate. |
| 參考文獻 |
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