Optimal Spectrum Sensing in Cognitive Radio Networks and Utility Optimization in Wireless Sensor Networks
thesisposted on 2016-06-21, 00:00 authored by Yingying Ma
In cognitive radio networks, spectrum sensing is the most significant element to detect the spectrum occupied by the primary users. The objective of our work is to jointly detect multibands with non-cooperative spectrum sensing and cooperative spectrum sensing. We are going to maximize the aggregate throughput of the secondary users over multiple bands, and simultaneously to limit the interference imposed to the primary users. Energy detection is applied to our study, which requires little prior information of the primary users. Designing appropriate thresholds of the energy detectors is critical to determine the performance of detection. The problem is formulated as a nonconvex optimization problem, which is intractable to solve, due to the high computational complexity. In this dissertation, we use the Taguchi method to estimate the gradient of the aggregate throughput function, and then determine the thresholds of the energy detectors and linear combination weights of the linear fusion rule. In noncooperative spectrum sensing, the cost function is defined as the aggregate throughput with thresholds of energy detectors as parameters. Multiple secondary user cooperative spectrum sensing is a more sophisticated case where the parameters of the thresholds and linear weights need optimization. In both noncooperative spectrum sensing and cooperative spectrum sensing, the Taguchi method is employed to find the optimal solution. One of the advantages of our approach is to optimize the thresholds and the linear weights simultaneously by solving the problem where the changing weights result in different limit ranges for the thresholds. The duration of sensing is another factor which affects the aggregate throughput. There exists a tradeoff that longer sensing duration can improve detection accuracy. However, it reduces transmission time of the secondary users, which essentially decreases the aggregate throughput. Thus, we also design an adaptive duration system to jointly optimize sensing duration with thresholds of energy detectors and the linear weights to further improve the aggregate throughput for the secondary users.