Energy detection constitutes a preferred approach for spectrum sensing in cognitive radio due to its simplicity and applicability (it works irrespective of the signal format to be detected) as well as its low computational and implementation costs. The main drawback, however, is its well-known detection performance limitations. Various alternative detection methods have been shown to outperform energy detection, but at the expense of increased complexity and confined field of applicability. In this context, this work proposes and evaluates an improved version of the energy detection algorithm that is able to outperform the classical energy detection scheme while preserving a similar level of algorithm complexity as well as its general applicability regardless of the particular signal format or structure to be detected. The performance improvement is evaluated analytically and corroborated with experimental results.
Cognitive Radio (CR) has been identified as a promising solution to the so-called spectrum scarcity problem. The basic idea of this paradigm is to allow unlicensed users to access in an opportunistic and non-interfering manner some licensed bands that are temporarily unoccupied by licensed users. CR is expected to significantly increase current spectrum usage. However, before this paradigm can turn into reality, a full understanding of the dynamic usage of spectrum is required. Current spectrum utilization has already been evaluated in some measurement campaigns. However, most of them were performed in USA and therefore evaluated the American spectrum regulation and utilization. Few studies have been carried out in other places. In this context, this paper presents spectrum occupancy measurements conducted in the frequency range from 75 MHz to 3 GHz in an outdoor environment in urban Barcelona, Spain. The measurements are analyzed and compared to the official spectrum regulations. The obtained results demonstrate the existence of a significant amount of spectrum available for the deployment of future CR networks.
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