Abstract
Lack of unallocated spectrum and increasing demand for bandwidth in wireless networks is forcing new devices and technologies to share frequency bands. Spectrum sensing is a key enabler for frequency sharing and there is a large body of existing work on signal detection methods. However a unified methodology that would be suitable for objective comparison of detection methods based on experimental evaluations is missing. In this paper we propose such a methodology comprised of seven steps that can be applied to evaluate methods in simulation or practical experiments. Using the proposed methodology, we perform the most comprehensive experimental evaluation of signal detection methods to date: we compare energy detection, covariance-based and eigenvalue-based detection and cyclostationary detection. We measure minimal detectable signal power, sensitivity to noise power changes and computational complexity using an experimental setup that covers typical capabilities from low-cost embedded to high-end software defined radio devices. Presented results validate our premise that a unified methodology is valuable in obtaining reliable and reproducible comparisons of signal detection methods.
Highlights
In recent years, we witnessed a drastic growth in the demand for bandwidth in wireless communications, mostly in consumer devices
Spectrum sensing is a key enabler for frequency sharing and there is a large body of existing work on signal detection methods
We proposed a unified methodology for objective quantitative evaluation of signal detection methods suitable for simulation or experimental set-ups
Summary
We witnessed a drastic growth in the demand for bandwidth in wireless communications, mostly in consumer devices. Due to the lack of unallocated spectrum, frequency bands are increasingly shared between different services [1]. Often frequencies are shared with legacy devices that have been designed under the assumption of exclusive frequency use. New devices and technologies face an interference avoidance problem. Spectrum sensing is a promising approach to solving this problem. It allows a device to detect the presence of other users and adapt its use of spectrum
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