Abstract
Collaborative spectrum sensing for detection of white spaces helps in realizing reliable and efficient spectrum sensing algorithms, which results in efficient usage of primary spectrum in secondary fashion. Collaboration among cognitive radios improves probability of detecting a spectral hole as well as sensing time. Available literature, in this domain, uses Gudmundson’s exponential correlation model for correlated lognormal shadowing under both urban and suburban environments. However, empirical measurements verify that the suburban environment can better be modeled through double exponential correlation model under suburban environments in comparison to Gudmundson’s exponential correlation model. Collaboration among independent sensors provides diversity gains. Asymptotic detection probability for collaborating users under suburban environments using double exponential correlation model has been derived. Also, the Region of Convergence performance of collaborative detection is presented which agrees well with analytical derivations.
Highlights
Cognitive radio is a revolutionary concept that aims to utilize licensed RF spectrum in an unlicensed/opportunistic fashion [1]
In Beacon-assisted based methods [2], primary user / licensed user transmits a beacon signal to cognitive users regarding available white-space on particular time and frequency bands that is decoded by the secondary users for successful exploitation of those spectral holes
This paper considers the application of double exponential correlation model under suburban environments for cognitive radio applications
Summary
Cognitive radio is a revolutionary concept that aims to utilize licensed RF spectrum in an unlicensed/opportunistic fashion [1]. In spectrum sensing based techniques [3]; cognitive radio users detect white spaces (either individually or collaboratively) and exploit the identified bands in opportunistic fashion. Cyclostationary feature based detector is an efficient and reliable method of spectrum sensing These detectors compute Spectral Correlation function of received signals which serves as the signature of the particular signals. These detectors can distinguish between primary user signals, noise and other interfering sources (by using the features of corresponding signatures) These gains are achieved on the basis of exact licensed user information as well as received computational complexity. Setting of threshold requires noise information only Computational simplicity makes these detectors a preferred choice for spectrum sensing cognitive radios. The detection probability (or misseddetection) for cognitive radio applications is computed using Gudmundson’s exponential correlation model [9] under both urban and suburban environments. M , Where represents the sensing time and shows the bandwidth. represents double exponential correlation covariance matrix with k x k measurements
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