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    Energy Consumption for Adaptive Transmission over Fading Channels: A Statistical Characterization

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    Type
    Conference Paper
    Authors
    Wang, Wen Jing
    Yang, Hong Chuan
    Alouini, Mohamed-Slim cc
    KAUST Department
    Electrical Engineering Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-07-16
    Online Publication Date
    2019-07-16
    Print Publication Date
    2019-05
    Permanent link to this record
    http://hdl.handle.net/10754/656766
    
    Metadata
    Show full item record
    Abstract
    With adaptive transmission, the transmission rate and/or power are adaptively adjusted according to the channel realization, which leads to a variable amount of energy consumption for the transmission of the same amount of data. We propose an analytical framework to statistically characterize the transmitter energy consumption of adaptive transmission over fading channels. For slow fading scenario, we derive the probability density function (PDF) and cumulative distribution function (CDF) of energy consumption assuming continuous-time Markov channel model. For the fast fading case, we apply the statistical mixture model to obtain the approximate PDF of energy consumption. Selected numerical results are presented to illustrate and to validate the mathematical formulations.
    Citation
    Wang, W.-J., Yang, H.-C., & Alouini, M.-S. (2019). Energy Consumption for Adaptive Transmission Over Fading Channels: A Statistical Characterization. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). doi:10.1109/icc.2019.8761582
    Publisher
    Institute of Electrical and Electronics Engineers Inc.
    Conference/Event name
    2019 IEEE International Conference on Communications, ICC 2019
    DOI
    10.1109/ICC.2019.8761582
    Additional Links
    https://ieeexplore.ieee.org/document/8761582/
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICC.2019.8761582
    Scopus Count
    Collections
    Conference Papers; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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