Extreme Eigenvalues-Based Detectors for Spectrum Sensing in Cognitive Radio Networks

This paper focuses on the design of the optimal or near-optimal detector resorting to extreme eigenvalues. A general framework for detector design involving model-driven and data-driven approaches is introduced. Specifically, the extreme eigenvalues based likelihood ratio test (LRT) is derived via the model-driven approach. Merging the model-driven and datadriven approaches, the Naive Bayesian detector is proposed based on the extreme eigenvalues, which converts the design of test statistic into a two-class decision boundary construction problem, and a solution is provided by the Naive Bayesian classifier. To render the detectors more practical, two near-optimal detectors called alpha-sum and a-product of maximum and minimum eigenvalues (alpha-SMME, alpha-PMME) are further designed, in which alpha is a weight coefficient. Furthermore, the theoretical performance analysis of the alpha-SMME and alpha-PMME algorithms is provided, and the optimal weight selection is further obtained by solving an optimization problem under the Neyman-Pearson criterion. Finally, simulation experiments demonstrate that the proposed detectors achieve performance improvements over the state-of-the-art detectors using extreme eigenvalues, and almost coincide with the detection performance of the LRT detector.

Automated summary

This paper focuses on the design of optimal or near-optimal detectors using extreme eigenvalues. A general framework involving model-driven and data-driven approaches is introduced. The extreme eigenvalues based likelihood ratio test (LRT) and Naive Bayesian detector are derived via the model-driven and merged approaches, respectively. Two near-optimal detectors called alpha-SMME and alpha-PMME are further designed for practicality. Theoretical performance analysis is provided and optimal weight selection is obtained for the alpha-SMME and alpha-PMME algorithms. Simulation experiments demonstrate the improved performance of the proposed detectors using extreme eigenvalues.