A Learning-Based Approach to Intra-Domain QoS Routing

Haipeng Yao, Huiwen Liu, Peiying Zhang, Sheng Wu, Chunxiao Jiang, Song Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)


In traditional networks, routing table is essential for packet transmission due to the lack of the direction information abut destination in the head of packet. However, it is feasible to make the address of device encode the routing information with the application of data technology. In this article, we propose new identities for networking routers -vectors, and a new routing principle based on these vectors is designed accordingly. These vectors encode the device distance information and serve as a pattern of the network topology. Then, routing decisions could be made by these vector calculations and only requirement of table query on the destination vector following the proposed routing principle. The proposed method is not limited in calculating the shortest path routing, but extend to solve the constrain routing problem. Besides, multi-paths routing is also available as long as multi-paths exist between the origin-destination pairs. The simulation results show that our proposed method works reliable and stable in routing tasks, and can achieve a remarkable performance when compared with the state-of-the-art work on the delay constrained least cost path (DCLC) problem.

Original languageEnglish
Article number9070161
Pages (from-to)6718-6730
Number of pages13
JournalIEEE Transactions on Vehicular Technology
Issue number6
Publication statusPublished - Jun 2020


  • Intra-domain network routing
  • machine learning
  • neural network
  • node vectors

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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