Primary User Traffic Classification in Dynamic Spectrum Access Networks

This paper focuses on analytical studies of the primary user (PU) traffic classification problem. colorblack{Observing} that the gamma distribution can represent positively skewed data and exponential distribution (popular in communication networks performance analysis literature) it is considered h...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications 2014-11, Vol.32 (11), p.2237-2251
Main Authors: Chun-Hao Liu, Pawelczak, Przemyslaw, Cabric, Danijela
Format: Article
Language:English
Subjects:
Analytical models
Dynamic spectrum access
Economic models
Estimation error
Hypotheses
Noise measurement
Parameter estimation
performance analysis
Probability density function
Random variables
Sensors
Statistical analysis
traffic classification
traffic estimation
traffic sampling
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Summary:This paper focuses on analytical studies of the primary user (PU) traffic classification problem. colorblack{Observing} that the gamma distribution can represent positively skewed data and exponential distribution (popular in communication networks performance analysis literature) it is considered here as the PU traffic descriptor. We investigate two PU traffic classifiers utilizing perfectly measured PU activity (busy) and inactivity (idle) periods: (i) maximum likelihood classifier (MLC) and (ii) multi-hypothesis sequential probability ratio test classifier (MSPRTC). Then, relaxing the assumption on perfect period measurement, we consider a PU traffic observation through channel sampling. For a special case of negligible probability of PU state change in between two samplings, we propose a minimum variance PU busy/idle period length estimator. Later, relaxing the assumption of the complete knowledge of the parameters of the PU period length distribution, we propose two PU traffic classification schemes: (i) estimate-then-classify (ETC), and (ii) average likelihood function (ALF) classifiers considering time domain fluctuation of the PU traffic parameters. Numerical results show that both MLC and MSPRTC are sensitive to the periods measurement errors when the distance among distribution hypotheses is small, and to the distribution parameter estimation errors when the distance among hypotheses is large. For PU traffic parameters with a partial prior knowledge of the distribution, the ETC outperforms ALF when the distance among hypotheses is small, while the opposite holds when the distance is large.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2014.141122