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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Zhang, Wenhao (Autor:in) / Vucetic, Branka (Autor:in) / Hardjawana, Wibowo (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    349156 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Real-time adaptive speed scheduler

    BAILEY LOUIS J / HALE RYAN D / SACCONE GREGORY T | Europäisches Patentamt | 2018

    Freier Zugriff

    A Scalable Grid Scheduler for Real-Time Applications

    Liu, C. / Baskiyar, S. | British Library Online Contents | 2009


    Robust Scheduler Design for Automatic Mechanical Transmission Real-time Control

    Xi, Gang / Zhang, Jianwu / Chen, Li et al. | SAE Technical Papers | 2006


    Robust scheduler design for automatic mechanical transmission real-time control

    Chen,L. / Xi,G. / Lu,T.L. et al. | Kraftfahrwesen | 2006


    A Real-Time Kernel Integrated with an Off-Line Scheduler

    Eriksson, C. / Lundback, K.-L. / Lawson, H. et al. | British Library Conference Proceedings | 1995