Learning to Schedule Resources in Software-Defined Radio Access Networks

Xianfu Chen

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


    A software-defined control plane simplifies network operations in dense radio access networks (RANs) by abstracting the base stations as a logical centralized network controller (CNC). In a software-defined RAN, the CNC and the wireless service providers (WSPs) can thus be decoupled. The CNC allocates subbands to the mobile terminals (MTs) based on their submitted bids. Such an auction is repeated across time and regulated by the Vickrey-Clarke-Groves pricing mechanism. The objective of an MT subscribed to a particular WSP is to optimize the expected long-term transmit power in transmitting packets subject to a specific Quality-of-Service constraint. We formulate the problem as a multi-agent Markov decision process, where the subband allocation (SA) and packet scheduling decisions are a function of the global network state. To address the challenges of signalling overhead and computational complexity, we approximate the queue state-SA factor by the sum of per-MT queue state value functions, and derive an online localized algorithm to learn them. The presented experiments show significant performance gains from our proposed studies.

    Original languageEnglish
    Title of host publication2018 IEEE Globecom Workshops, GC Wkshps 2018 - Proceedings
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Number of pages6
    ISBN (Electronic)978-1-5386-4920-6, 978-1-5386-6977-8
    Publication statusPublished - 2 Jul 2018
    MoE publication typeA4 Article in a conference publication
    Event2018 IEEE Globecom Workshops, GC Wkhps 2018 - Abu Dhabi, United Arab Emirates
    Duration: 9 Dec 201813 Dec 2018


    Conference2018 IEEE Globecom Workshops, GC Wkhps 2018
    Country/TerritoryUnited Arab Emirates
    CityAbu Dhabi


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