Spectrum Occupancy Measurements and Cognitive Radio System Implementation

Abstract

Nowadays, radio spectrum is mostly crowded and occupied by many fixed wireless services. Therefore, there is less opportunity of finding a vacant band (spatially or temporally) for deploying new wireless communication services or enhancing already existing ones. The Telecommunications Regulatory Authority (TRA) allocation chart in UAE shows some overlapping allocation for services given the same band which reinforces the spectrum scarcity concept. Insufficient frequency spectrum allocation and the problem of spectrum scarcity are standing against the will of introducing more services to the wireless communication community. As a result, many measurement campaigns around the world have been conducted in order to investigate more about the spectrum utilization and characterization. Dynamic Spectrum Access (DSA) technologies have been introduced and promised to use the idle spectrum bands and utilize them efficiently. One form of DSA technologies is Cognitive Radio (CR) which is based on allowing an unlicensed (secondary) user to access an unoccupied portion of licensed spectrum and use it without causing interference with the licensed (primary) user in an opportunistic way. This thesis is mainly divided into two parts; in the first part, the occupancy of the frequency spectrum is studied through multiple measurement campaigns. These campaigns lasted for twenty days and conducted at the American University of Sharjah. These measurements were done over the ultra-high frequency (UHF) due its potential to be utilized by cognitive radio systems. The measurements indicated that large portions of the UHF band are not utilized efficiently. A Gaussian mixture model (GMM) analysis was carried out to obtain quantitative observations about the UHF occupancy levels. The second part of this thesis is about implementing a cognitive radio system based on real data collected using a prepared experimental setup consists of Universal Software Radio Peripheral (USRP) devices. An energy detector and polynomial classifier were implemented for spectrum sensing. A comparison between the two approaches shows that polynomial classifier has better performance over the energy detector in terms of the misclassification rate.