4.7 Article

User Association, Subchannel and Power Allocation in Space-Air-Ground Integrated Vehicular Network With Delay Constraints

期刊

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TNSE.2022.3169635

关键词

Resource management; Edge computing; Delays; Task analysis; Computer architecture; Quality of service; Optimization; Delay constraint; edge computing; resource allocation; space-air-ground integrated vehicular networks

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This paper focuses on the resource allocation problem in the space-air-ground integrated vehicular networks (SAGVN). It proposes a user association and subchannel/power allocation scheme to optimize the connection and communication performance of small cells. Edge computing is also applied to offload local tasks to improve communication performance.
The space-air-ground integrated vehicular networks (SAGVN) has significant advantages in satisfying the requirements of the Internet of Vehicles (IoV) business for wide area coverage and long-distance communications. However, the multi-dimensional nature of the SAGVN leads to diverse network resources, it is necessary to provide different services for various scenarios, which makes the problem of network resource allocation becomes extremely challenging. In this paper, we focus on the resource allocation of small cells in SAGVN. Specifically, the user association is considered to optimize connection between base stations and vehicles. On this basis, a subchannel and power allocation method is designed. The proposed subchannel allocation scheme ensures that users can obtain the maximum gain on its subchannel, and the Lagrangian duality theory is introduced to solve the power allocation problem. Meanwhile, the edge computing is applied to the SAGVN, and the vehicles can offload the local tasks to the edge server. In order to reduce the delay in the offloading process, taking the time delay as a constraint condition can further optimize the communication performance. The simulation results show that the proposed scheme can effectively improve the sum rate of small cells in SAGVN, and the performance is better than existing algorithms.

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