Wireless resource scheduling in virtualized radio access networks using stochastic learning

Xianfu Chen, Zhu Han, Honggang Zhang, Guoliang Xue, Yong Xiao, Mehdi Bennis

    Research output: Contribution to journalArticleScientificpeer-review

    43 Citations (Scopus)


    How to allocate the limited wireless resource in dense radio access networks (RANs) remains challenging. By leveraging a software-defined control plane, the independent base stations (BSs) are virtualized as a centralized network controller (CNC). Such virtualization decouples the CNC from the wireless service providers (WSPs). We investigate a virtualized RAN, where the CNC auctions channels at the beginning of scheduling slots to the mobile terminals (MTs) based on bids from their subscribing WSPs. Each WSP aims at maximizing the expected long-term payoff from bidding channels to satisfy the MTs for transmitting packets. We formulate the problem as a stochastic game, where the channel auction and packet scheduling decisions of a WSP depend on the state of network and the control policies of its competitors. To approach the equilibrium solution, an abstract stochastic game is proposed with bounded regret. The decision making process of each WSP is modeled as a Markov decision process (MDP). To address the signalling overhead and computational complexity issues, we decompose the MDP into a series of single-agent MDPs with reduced state spaces, and derive an online localized algorithm to learn the state value functions. Our results show significant performance improvements in terms of per-MT average utility.
    Original languageEnglish
    Pages (from-to)961-974
    Number of pages14
    JournalIEEE Transactions on Mobile Computing
    Issue number4
    Early online date2017
    Publication statusPublished - 2018
    MoE publication typeA1 Journal article-refereed


    • wireless communication
    • stochastic processes
    • games
    • radio access networks
    • mobile computing
    • mobile communication
    • scheduling


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