Vehicular communication has the potential to revolutionize road safety, traffic efficiency, infotainment services, and autonomous driving. However, as urban traffic complexity increases, intelligent transportation solutions are essential. Priority-based resource allocation is crucial for optimizing network performance and information transmission efficiency in the Internet of Vehicles (IoV). In this context, Q- learning optimizes resource allocation in vehicular communication systems, prioritizing real-time traffic conditions. The Q-learning model effectively allocates resources based on priority, continuously improving strategy over time. This study shows the significant potential of the Q-Learning model for optimizing priority-based resource allocation in vehicular communication, enhancing road safety and efficiency.


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    Title :

    Priority-Based Resource Allocation in Vehicular Communication Using Q-Learning


    Contributors:


    Publication date :

    2024-08-08


    Size :

    783569 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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