Design of an Optimal Eigen-value-based Spectrum Sensing Algorithm for Cognitive Radio
Spectrum is a scarce resource, and licensed spectrum is intended to be used only by the spectrum owners. The Communications Authority of Kenya (CAK) for example, encourages radio frequency spectrum sharing among various services and users in order to satisfy the growing needs for spectrum resources. Various measurements of spectrum utilization have shown unused resources in frequency, time and space. The unused resources are often referred to as spectrum holes or white spaces. Cognitive radio can be employed in identifying these spectrum holes which can then be used by secondary users. The introduction of cognitive radios in a primary user network will inevitably have an impact on the primary system, for example in terms of increased interference. Cognitive radios must be able to detect very weak primary user signals, to be able to keep the interference power at an acceptable level. One of the most essential parts of cognitive radio is spectrum sensing. All man-made signals have some structure that can be potentially exploited to improve the 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. Spectrum sensing methods based on the eigenvalues of the sample covariance matrix have been proposed and analyzed in recent research. This thesis presents an optimal eigenvalue-based spectrum sensing algorithm that is based on power methods for computation of the dominant eigenvalue of the covariance matrix of signals received at the secondary user. Compared with other methods discussed in literature, this detection method is proposed for a cognitive user equipped with a single receiving antenna thereby greatly reducing system overheads unlike methods that require multiple antenna systems. Secondly, the presented algorithm has a lower computational complexity since it avoids eigenvalue decomposition. Finally, simulation results show that the presented algorithm performs better than the ideal energy detection method and its performance is very close to that of the maximum-minimum eigenvalue detection method which uses multiple antennas.
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