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Whitespace Prediction Using Hidden Markov Model Based Maximum Likelihood Classification

: Saad, Ahmad; Schepker, Henning F.; Staehle, Barbara; Knorr, Rudi


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 89th Vehicular Technology Conference, VTC-Spring 2019. Proceedings : Kuala Lumpur, Malaysia, 28 April-1 May 2019
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-1217-6
ISBN: 978-1-7281-1216-9
ISBN: 978-1-7281-1218-3
7 pp.
Vehicular Technology Conference (VTC Spring) <89, 2019, Kuala Lumpur>
Conference Paper
Fraunhofer ESK ()
cognitive Radio; whitespace prediction; Hidden Markov Model; HMM; maximum likelihood; ML; maximum likelihood classification

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.