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Analysis of wireless communication networks under edge computing scenarios


In communication networks, edge computing is a method of distributing processing, storage, and bandwidth resources to the side of the network that is closest to the user. This could be achieved through small computing devices installed in proximity of user or the network routers. In this paper, we discuss the architecture and principles of mobile edge computing (MEC) and edge caching in large-scale wireless networks. Moreover, we also discuss the necessity, widespread use, and the future of MEC and caching technologies. Finally, we analyze five key issues when MEC and caching are used for large-scale wireless networks: (i) computation offloading, (ii) edge caching, (iii) multidimensional resource allocation, (iv) user association (including privacy protection), and (v) privacy protection. Furthermore, edge servers are typically equipped with limited resources and are unable to meet the service demands of all vehicular network users at the same time. Therefore, identifying locations for service offloading and providing low latency services to users, while working within the aforementioned constraints, continues to be a significant challenge. Using deep and deep intensive learning coordination, we propose an edge computing system model for 5G vehicular networks that includes an “end-to-edge cloud” coordination. We, then, suggest a distributed service offloading method, called DSOAC, based on the coordination. Based on data sets collected from real-world wireless communication networks, experimental findings reveal that the DSOAC method can decrease service offloading time by approximately 0.4 to 20.0% when compared to the four current service offloading techniques. We observed that this ranges from 4 to 20.4% of the typical user workloads. An approximate 4% of the average customer service delay was caused by the proposed offloading scheme.

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Research on innovation countermeasures of library information Service from the perspective of big data”(Project No:SLGKY16-40).

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Correspondence to Jianhang Wang.

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Wang, J. Analysis of wireless communication networks under edge computing scenarios. Wireless Netw (2022).

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  • Edge computing
  • Wireless networks
  • End-to-edge
  • Offloading