The O-RAN architecture standardized by the O-RAN Alliance does not support the latency requirement of real-time fifth-generation (5G) edge intelligence, typically at a msec level. In this paper, we proposed a deep reinforcement learning (DRL) packet scheduler framework to manage users with different quality of service (QoS) requirements. The DRL framework uses an advantage actor-critic (A2C) algorithm, referred to as a QoS-A2C scheduler. The developed QoS-A2C scheduler is then proposed as an O-RAN real-time App at the edge network. Simulation results show that the latency of the App is at $\mu\sec$ level. It also improves the QoS satisfaction level by more than 50% compared to other DRL-based scheduler schemes.


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    Title :

    5G Real-Time QoS-Driven Packet Scheduler for O-RAN


    Contributors:


    Publication date :

    2024-06-24


    Size :

    349156 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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