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    Energy Consumption Analysis for Adaptive Transmission of Big Data over Fading Channels: A Statistical Characterization

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    Type
    Conference Paper
    Authors
    Wang, Wen-Jing cc
    Yang, Hong Chuan cc
    Alouini, Mohamed-Slim cc
    KAUST Department
    Communication Theory Lab
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2019-12-12
    Online Publication Date
    2019-12-12
    Print Publication Date
    2020-06
    Submitted Date
    2019-07-06
    Permanent link to this record
    http://hdl.handle.net/10754/663820
    
    Metadata
    Show full item record
    Abstract
    We study the energy consumption of the adaptive transmission of big data over fading channels. Transmission of big data usually lasts over multiple channel coherence time periods. With adaptive transmission, the transmission rate and/or power is adaptively adjusted according to the channel realization. The energy comsumption varies with channel realizations. We propose a novel analytical framework to statistically evaluate the energy consumption of adaptive big data transmission. For the slow fading scenario, we derive the exact probability density function (PDF) and cumulative distribution function (CDF) of energy consumed over Markov channels. For the fast fading cases, we apply the statistical mixture model to estimate the PDF of energy consumption for wireless transmission of big data adaptively. Selected numerical results are presented to illustrate and to validate the mathematical formulations. These analytical results will greatly benefit the study of big data applications in the wireless environment.
    Citation
    Wang, W.-J., Yang, H.-C., & Alouini, M.-S. (2020). Energy Consumption Analysis for Adaptive Transmission of Big Data Over Fading Channels: A Statistical Characterization. IEEE Transactions on Green Communications and Networking, 4(2), 365–374. doi:10.1109/tgcn.2019.2959077
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    DOI
    10.1109/TGCN.2019.2959077
    Additional Links
    https://ieeexplore.ieee.org/document/8931739/
    ae974a485f413a2113503eed53cd6c53
    10.1109/TGCN.2019.2959077
    Scopus Count
    Collections
    Conference Papers; Electrical and Computer Engineering Program; Communication Theory Lab; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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