Abstract
Principal component (PC) algorithm has recently been shown as a very accurate blind detection technique in comparison with other covariance-based detection algorithms. However, it also has a higher complexity owing to the computation of the eigenvectors. We propose a low-complexity Lanczos principal component (LPC) algorithm that utilizes Lanczos iterative method to compute the eigenvectors. In comparison with the PC algorithm, the proposed LPC algorithm offers significant reduction in complexity while giving a similar detection performance. Low-complexity LPC algorithm allows for the use of larger sized covariance matrix that further improves the detection performance. Maximum-minimum eigenvalue (MME) algorithm is also included in the comparison and it gives an inferior performance as compared to both PC and LPC algorithm. All the algorithms were tested with experimental data while using universal software radio peripheral (USRP) testbed that was controlled by GNU radio software.
Highlights
Cognitive radio has the ability to communicate over the unused frequency spectrum intelligently and adaptively
The performance of Lanczos principal component (LPC) is compared with Principal component (PC) as well as minimum eigenvalue (MME) algorithms
The results obtained from the PC and LPC algorithms were with G = 2 and the number of samples used in making a single detection decision N was kept constant for all the three algorithms
Summary
Cognitive radio has the ability to communicate over the unused frequency spectrum intelligently and adaptively. Spectrum sensing in a cognitive radio (CR) is crucial in generating awareness about the radio environment [1] Blind detection methods such as covariance-based detection (CBD) algorithms enable signal detection in low signal-to-noise ratio (SNR) conditions without relying on the prior knowledge of the primary user’s (PU) signal. CBD techniques overcome the issue of noise power uncertainty that exists in an energy detector [2, 3]. These methods use the covariance and variances of the received signal and do not require information about the noise variance. Spectrum sensing can help eradicate collisions and excessive contention delay experienced by dense node deployment Such devices/sensors have embedded computing nature, energy efficiency is their major concern. Low complexity, energy efficient spectrum sensing algorithms are vital for implementation in such devices
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