A network slicing elastic switching algorithm based on deep reinforcement learning is proposed to address the network disconnection problem caused by the mobility of VR end users, which requires fast network switching to ensure service continuity. The slicing switching problem is modeled as a Markov decision process (MDP), which matches users, slices, and base stations. Introducing Double Deep Q-network (DDQN) into software defined network (SDN) controllers to achieve intelligent network resilient switching, improving system access success rate, average rate, and service continuity. The simulation results show that the proposed algorithm has a success rate of over 85% in access, and there is also a significant improvement in user speed and service continuity.


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    Title :

    A Network Slicing Elastic Switching Algorithm for VR Devices Based on DDQN


    Contributors:
    Zhou, Yuanyuan (author) / Li, Xiaohui (author) / Lv, Siting (author) / He, Guodong (author) / Shi, Mingli (author) / Chen, Xingbo (author)


    Publication date :

    2024-06-24


    Size :

    1195036 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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