The aim of this paper is to present a groundwork on the delay-minimized routing problem in a vehicular ad-hoc network (VANET) where some of the vehicles are equipped with full-duplex (FD) radios. We first give the generalized delay calculation model for a multi-hop path, and prove that the Dijkstra algorithm is unable to get the delay-minimized routing path from source to destination. Then we propose two routing methods: graph-based method and deep reinforcement learning (DRL)-based method. In the graph-based method, the network topology is reformulated as an equivalent graph and then an evolved-Dijkstra algorithm is proposed. In the DRL-based method, the deep Q network (DQN) is employed to learn the shortest end-to-end path, wherein the delay is modeled as the rewards for routing actions. The graph-based method can achieve the exact minimum end-to-end delay, while the DRL-based method is more feasible due to its acceptable complexity. Finally, extensive simulations demonstrate that the DRL-based approach with proper hyper-parameters can achieve near minimum end-to-end delay, and the achieved delay has a notably decline as the number of FD nodes increases.


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    Titel :

    On Vehicular Ad-Hoc Networks With Full-Duplex Radios: An End-to-End Delay Perspective


    Beteiligte:
    Zhou, Momiao (Autor:in) / Liu, Lei (Autor:in) / Sun, Yanshi (Autor:in) / Wang, Kan (Autor:in) / Dong, Mianxiong (Autor:in) / Atiquzzaman, Mohammed (Autor:in) / Dustdar, Schahram (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2023-10-01


    Format / Umfang :

    6457521 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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