Cellular vehicular-to-everything (C-V2X), a critical Internet of Vehicles (IOV) technology is promised to be enhanced and strengthened to improve road traffic safety and achieve intelligent transportation in the 5G era. However, computation-intensive and latency-sensitive computation tasks of autonomous driving have created a great challenge for computation and storage-limited vehicles. Vehicular edge computing (VEC) is envisioned as a promising approach to processing the explosive computation tasks of vehicular users (VU). In the VEC system, each VU allocates to process partial tasks through offloading and the remaining tasks through local execution. In practical scenarios, the number of vehicles and the arrival of vehicle tasks are random, leading to a highly complex environment for VEC systems. To solve this problem, we propose a novel framework, named LYDDPG, that combines the advantages of Lyapunov optimization and deep reinforcement learning (DRL) to ensure the stability of the system during task offloading.


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    Title :

    Lyapunov-guided Deep Reinforcement Learning for Vehicle task Stable offloading


    Contributors:


    Publication date :

    2024-05-08


    Size :

    2419313 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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