Distributed Learning for Energy-Efficient Resource Management in Self-Organizing Heterogeneous Networks

Atefeh Hajijamali Arani, Abolfazl Mehbodniya, Mohammad Javad Omidi, Fumiyuki Adachi, Walid Saad, Ismail Guvenc

    Research output: Contribution to journalArticlepeer-review

    24 Citations (Scopus)

    Abstract

    In heterogeneous networks, a dense deployment of base stations (BSS) leads to increased total energy consumption, and, consequently, increased cochannel interference (CCI). In this paper, to deal with this problem, self-organizing mechanisms are proposed, for joint channel and power allocation procedures, which are performed in a fully distributed manner. A dynamic channel allocation mechanism is proposed, in which the problem is modeled as a noncooperative game, and a no-regret learning algorithm is applied for solving the game. In order to improve the accuracy and reduce the effect of shadowing, we propose another channel allocation algorithm executed at each user equipment (UE). In this algorithm, each UE reports the channel with minimum CCI to its associated BS. Then, the BS selects its channel based on these received reports. To combat the energy consumption problem, BSS choose their transmission power by employing an on-off switching scheme. Simulation results show that the proposed mechanism, which is based on the second proposed channel allocation algorithm and combined with the on-off switching scheme, balances load among BSS. Furthermore, it yields significant performance gains up to about 40.3%, 44.8% , and 70.6% in terms of average energy consumption, UE's rate, and BS's load, respectively, compared to a benchmark based on an interference-Aware dynamic channel allocation algorithm.

    Original languageEnglish
    Article number7907168
    Pages (from-to)9287-9303
    Number of pages17
    JournalIEEE Transactions on Vehicular Technology
    Volume66
    Issue number10
    DOIs
    Publication statusPublished - 2017 Oct

    Keywords

    • Co-channel interference
    • energy efficiency
    • heterogeneous networks
    • learning algorithm

    ASJC Scopus subject areas

    • Automotive Engineering
    • Aerospace Engineering
    • Electrical and Electronic Engineering
    • Applied Mathematics

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