Dynamic Spectrum Access Network Simulation and Classification of Secondary User Properties

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Virginia Tech


This thesis explores the use of the Naïve Bayesian classifier as a method of determining high-level information about secondary users in a Dynamic Spectrum Access (DSA) network using a low complexity channel sensing method.  With a growing number of users generating an increased demand for broadband access, determining an efficient method for utilizing the limited available broadband is a developing current and future issue.  One possible solution is DSA, which we simulate using the Universal DSA Network Simulator (UDNS), created by our team at Virginia Tech.

However, DSA requires user devices to monitor large amounts of bandwidth, and the user devices are often limited in their acceptable size, weight, and power.  This greatly limits the usable bandwidth when using complex channel sensing methods.  Therefore, this thesis focuses on energy detection for channel sensing.

Constraining computing requirements by operating with limited spectrum sensing equipment allows for efficient use of limited broadband by user devices.  The research on using the Naïve Bayesian classifier coupled with energy detection and the UDNS serves as a strong starting point for supplementary work in the area of radio classification.



Dynamic Spectrum Access, Cognitive Radios, Naïve Bayesian Classification, Matlab Simulation