An Optimal Eigenvalue Based Spectrum Sensing Algorithm for Cognitive Radio

Abstract

Spectrum is a scarce resource, and licensed spectrum is intended to be used only by the spectrum owners. Various measurements of spectrum utilization have shown unused resources in frequency, time and space. Cognitive radio is a new concept of reusing licensed spectrum in an unlicensed manner. The unused resources are often referred to as spectrum holes or white spaces. These spectrum holes could be reused by cognitive radios, sometimes called secondary users. All man-made signals have some structure that can be potentially exploited to improve their detection performance. This structure is intentionally introduced for example by the channel coding, the modulation and by the use of space-time codes. This structure, or correlation, is inherent in the sample covariance matrix of the received signal. In particular the eigenvalues of the sample covariance matrix have some spread, or in some cases some known features that can be exploited for detection. This work aims to implement, evaluate, and eventually improve on algorithms for efficient computation of eigenvalue-based spectrum sensing methods. The computations will be based on power methods for computation of the dominant eigenvalue of the covariance matrix of signals received at the secondary users. The proposed method endeavors to overcome the noise uncertainty problem, and perform better than the ideal energy detection method. The method should be used for various signal detection applications without requiring the knowledge of the signal, channel and noise power.