Performance of Cognitive Radio Networks with Unknown Dynamic Primary User Signals

The current static assignment of RF spectrum in the United States and other parts of the world has led to a large portion of the RF spectrum to be geographically and temporally underutilized. While the amount of RF spectrum is finite, the demand for spectrum continues to increase making it necessary to increase utilization of many bands. Several innovative methods for allowing licensed primary users (PUs) to share spectrum with unlicensed secondary users (SUs) have being proposed. Of these methods Cognitive Radio (CR) has emerged as a promising technology that enables SUs to dynamically access spectrum after first sensing the spectrum to ensure the PU is not active. Sensing performance is critical to a successful CR implementation, and within the last decade there has been significant CR research examining various sensing challenges and methods to improve sensing performance. The majority of this research has focused on PUs that utilize spectrum with relatively long idle and transmission periods which in turn allows for SU sensing periods with an extended duration. The work presented in this dissertation focuses on CR systems where the PU is highly dynamic and addresses several issues that arise when attempting to access this spectrum. In the case of a highly dynamic PU, it is not possible for the SU to increase the sensing period to improve performance, resulting in suboptimal sensing performance. A proposed hybrid framework is described which allows for suboptimal sensing performance by limiting the SU transmission power dependent on the sensing capabilities. In order to quantify sensing capabilities, a mathematical model for describing the PU activity with respect to the SU sensing period is derived using the mean active and idle durations of the PU. Using this PU activity model, closed form mathematical expressions for sensing performance are provided for two different hypothesis tests. Finally, the PU activity model and corresponding expressions for sensing performance depend on knowing the mean PU active and idle durations; because the SU may not know these PU parameters, a modified expectation maximization algorithm is proposed to estimate these parameters and corresponding sensor performance