The cornerstone of cognitive systems is environment awareness which enables agile and adaptive use of channel resources. Whitespace prediction based on learning the statistics of the wireless traffic has proven to be a powerful tool to achieve such awareness. In this paper, we propose a novel HiddenMarkov Model (HMM) based spectrum learning and prediction approach which accurately estimates the exact length of the whitespace in WiFi channels within the shared industrial scientific medical (ISM) bands. We show that extending the number of hidden states and formulating the prediction problem as a maximum likelihood (ML) classification leads to a substantial increase in the prediction horizon compared to classical approaches that predict the immediate (short-term) future. We verify the proposed algorithm through simulations which utilize a model for WiFi traffic based on extensive measurement campaigns.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Whitespace Prediction Using Hidden Markov Model Based Maximum Likelihood Classification


    Contributors:


    Publication date :

    2019-04-01


    Size :

    467047 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Motion Prediction of Tugboats Using Hidden Markov Model

    Zhang, Zijian / Zhao, Jie / Wang, Tengfei et al. | IEEE | 2023


    Maximum-Likelihood Image Classification

    Wernick, Miles N. / Morris, G. M. | SPIE | 1988



    Improvement of Attitude Estimation using Hidden Markov Model Classification

    Kang, C. / Park, C. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2010


    Freeway traffic flow prediction based on hidden Markov model

    Jiang, Jiyang / Guo, Tangyi / Pan, Weipeng et al. | British Library Conference Proceedings | 2022