4.5 Article

5G Mobile Virtual Reality Optimization Solution for Communication and Computing Integration

期刊

MOBILE NETWORKS & APPLICATIONS
卷 27, 期 3, 页码 912-925

出版社

SPRINGER
DOI: 10.1007/s11036-021-01812-7

关键词

5G network; Mobile cloud computing; Task offload allocation; Random access optimization

资金

  1. 2019 Scientific research fund project in Yunnan province department of education: The protection and inheritance of Nixi black pottery based on AR technology [2019 J0056]

向作者/读者索取更多资源

This paper introduces a hierarchical distributed cloud service network model for 5G networks and a task offloading assignment algorithm in the small cell cloud scenario. The proposed solutions aim to improve resource utilization and user experience quality, as well as ensure the system operates normally under heavy load conditions.
5G network is an inevitable trend in the development of mobile communications. Mobile cloud computing is a more promising technology for 5G networks. This paper proposes a hierarchical distributed cloud service network model, which is composed of three layers: access cloud + distributed micro cloud + core cloud. On the basis of access to the cloud, a distributed micro cloud system is deployed to migrate the service capabilities of the remote core cloud server to the local area. This paper proposes a task offloading assignment algorithm in a small cell cloud scenario. This algorithm establishes a SCC (Small Cell Cloud) based on the channel quality between small cells and the remaining available computing resources, and allocates the load to each small cell in the SCC according to the channel quality and the remaining available computing resources. Simulation results show that this solution can improve the utilization of wireless and computing resources in the small cell cloud computing scenario, and improve the user QoE (Quality of Experience). In order to make the system operate normally under heavy load, this paper proposes a feedback adaptive random access strategy based on the adaptive random access model. This can ensure that the throughput rate does not decrease under heavy load conditions, and at the same time, the average access delay of the existing system is reduced. When the arrival rate of user requests gradually increases, the throughput rate of RA-RACH access will continue to decrease due to collisions until it approaches below 0.1. In the state where the number of users is low and the load is lighter, both RA-RACH, AC-RACH, and FC-RACH have a higher access success rate. But as the load continues to increase, RA-RACH will quickly drop to 0.

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