SPECTRUM OBSERVATORY BASED TRAFFIC MODELING AND CHANNEL SELECTION IN SUPPORT OF DYNAMIC SPECTRUM ACCESS

It is well known that the exponential growth in popularity of wireless devices has created a demand for radio spectrum that cannot be met with current regulatory policies. Despite the difficulty in procuring access to new spectrum resources, many empirical studies have indicated that the majority of spectrum is in-fact unused in the temporal, spatial and/or spectral domains, representing an untapped wealth that must be exploited. Dynamic Spectrum Access (DSA) is a promising technology which aims to improve the efficiency of future radios and alleviate the issue of spectrum under-utilization. This dissertation utilizes the data from the IIT Spectrum Observatory to develop models of channel activity on the Land Mobile Radio (LMR) band (used for critical communication by organizations such as public safety) and shows how such models can be applied to improve the performance of DSA. We demonstrate that LMR traffic may possess multi-timescale behavior – such as clustering and dispersion over different time periods – and propose a novel statistical model to account for these observations based on a multiple emission hidden Markov model. We then used this model to design a collision constrained channel selection algorithm that can permit the re-use of licensed spectrum while minimizing interference with incumbent users. The findings in this work are primarily developed for public safety, however the techniques developed are general enough to be applied to other types of traffic possessing similar characteristics. The proposed model, in particular, is well suited for further analytic work and simulations studies in this area.Ph.D. in Electrical and Computer Engineering, May 201