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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2022, Vol. 43 ›› Issue (3): 325123.doi: 10.7527/S1000-6893.2021.25123

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Aviation information network slicing strategy supporting differentiated services

SONG Xinkang, ZHAO Shanghong, WANG Xiang   

  1. College of Information and Navigation, Air Force Engineering University, Xi'an 710077, China
  • Received:2020-12-18 Revised:2021-01-07 Online:2022-03-15 Published:2021-03-01
  • Supported by:
    National Natural Science Foundation of China (91638101); Shaanxi Natural Science Foundation of China (2020 JQ-483)

Abstract: Considering the difference of network performance requirements of various combat missions in aviation cluster, the problem of aviation information network slicing with flexible coupling mission requirements is studied. According to the task characteristics, the network slice types and performance characteristics are defined, and an integer programming model for slice performance optimization is constructed with task resource demand as the constraint. The network slicing problem is decomposed into two steps:platform resource clustering and link resource clustering. A platform resource clustering algorithm is proposed based on particle swarm optimization, and a link resource clustering algorithm is proposed based on perceptive nodes. In the stage of platform resource clustering, the iterative solution is obtained by using the particle clustering algorithm based on clustering factor sequencing. In the link resource clustering stage, the performance of each network slice and the link load rate are optimized, and link resource clustering is completed on the basis of platform resource clustering. The results show that the aviation information network slice constructed by the proposed algorithm can meet the requirements of different tasks for platform resources and network transmission capacity, significantly improving the utilization rate of platform resources and link load rate.

Key words: aviation cluster, network function virtualization, network slicing, differentiated service, resource clustering

CLC Number: