Statistical modeling of ISM data traffic in indoor environments for cognitive radio systems

To quantify spectrum usage, many outdoor and indoor measurement campaigns have already been conducted in different parts of the world. These studies assist policy makers in optimizing spectrum management policies by providing necessary information about the usage patterns of wireless services in different spectrum bands. Furthermore, the spectrum usage measurements help researchers to build a mechanism for efficient dynamic spectrum access in cognitive radio (CR) systems based on prior knowledge of the distribution of the observed data traffic. In this paper, we statistically model the data traffic observed in the industrial, scientific and medical (ISM) band at 2.4 GHz. Since the measured ISM data traffic is short-range dependant, its frequency and time correlation functions are modeled using an exponentially decaying function. The multivariate Gaussian mixture (MGM) approach is found not only to model the joint distribution of the multivariate ISM data traffic, but also to accurately estimate the correlation between the neighboring frequency subbands and neighboring time slots, respectively.