Title: A FRAMEWORK FOR STATISTICAL CHARACTERIZATION OF INDOOR DATA TRAFFIC FOR EFFICIENT DYNAMIC SPECTRUM ACCESS IN THE 2.4 GHZ ISM BAND

Issue Number: Vol. 5, No. 4
Year of Publication: Oct - 2015
Page Numbers: 210-220
Authors: Muhammad Khurram Ehsan , Dirk Dahlhaus
Journal Name: International Journal of Digital Information and Wireless Communications (IJDIWC)
- Hong Kong
DOI:  http://dx.doi.org/10.17781/P001712

Abstract:


The key for efficient dynamic spectrum access (DSA) is to model the spectral resources accurately. A large number of measurement campaigns have been performed to estimate the spectrum usage in outdoor and indoor scenarios. This spectrum usage estimation helps policy makers to optimize the spectrum management methodologies. The spectrum usage studies also assist researchers to constitute a way for efficient DSA using prior knowledge of the distribution of the observed data traffic in cognitive radio (CR) systems. In this paper we extend our previous work which statistically modeled the observed data traffic in the industrial, scientific and medical (ISM) band at 2.4 -GHz in two neighboring frequency subbands and time slots, respectively, to three neighboring frequency subbands and time slots, respectively. As before, the frequency and time correlation functions of the observed data traffic are modeled by an exponentially decaying function. The multivariate Gaussian mixture (MGM) is validated as a good candidate to model the joint distribution of measured data and also to estimate the correlation between the measured data in neighboring frequency subbands and as well as in time domain samples.