Abstract
Spectrum sensing is a core function at cognitive radio systems to have spectrum awareness. This could be achieved by collecting samples from the frequency band under observation to make a conclusion whether the band is occupied, or it is a spectrum hole. The task of sensing is becoming more challenging especially at wideband spectrum scenario. The difficulty is due to conventional sampling rate theory which makes it infeasible to sample such very wide range of frequencies and the technical requirements are very costly. Recently, compressive sensing introduced itself as a pioneer solution that relaxed the wideband sampling rate requirements. It showed the ability to sample a signal below the Nyquist sampling rate and reconstructed it using very few measurements. In this paper, we discuss the approaches used for solving compressed spectrum sensing problem for wideband cognitive radio networks and how the problem is formulated and rendered to improve the detection performance.
Highlights
Spectrum sensing is a core function at cognitive radio systems to have spectrum awareness. This could be achieved by collecting samples from the frequency band under observation to make a conclusion whether the band is occupied, or it is a spectrum hole
Even though there are numerous research papers that discussed the problem of spectrum sensing for wideband cognitive radio based compressed sensing, the detection problem is formulated as detection based-signal reconstruction using compressed sensing which is the prevailing trend [5] [6] [18] [23]
To the best of our knowledge most of the researches are dedicated to improving the detection performance of the energy detector
Summary
To solve the spectrum issue, the Federal Communications Commission (FCC) suggested opening these frequency bands dedicated to the PUs to be shared with Secondary Users (SUs). Numerous research papers were published to identify spectrum holes-based CS through signal reconstruction algorithms. This kind of spectrum holes identification dominant since . Even though there are numerous research papers that discussed the problem of spectrum sensing for wideband cognitive radio based compressed sensing, the detection problem is formulated as detection based-signal reconstruction using compressed sensing which is the prevailing trend [5] [6] [18] [23].
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