There are new system implementation challenges involved in the design of cognitive radios, which have both the ability to sense the spectral environment and the flexibility to adapt transmission parameters to maximize system capacity while co-existing with legacy wireless networks. The critical design problem is the need to process multi-gigahertz wide bandwidth and reliably detect presence of primary users. This places severe requirements on sensitivity, linearity, and dynamic range of the circuitry in the RF front-end. To improve radio sensitivity of the sensing function through processing gain we investigated three digital signal processing techniques: matched filtering, energy detection, and cyclostationary feature detection. Our analysis shows that cyclostationary feature detection has advantages due to its ability to differentiate modulated signals, interference and noise in low signal to noise ratios. In addition, to further improve the sensing reliability, the advantage of a MAC protocol that exploits cooperation among many cognitive users is investigated.

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Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with primary users, by reliably detecting primary user signals. Recent research studied spectrum sensing using energy detection and network cooperation via modeling and simulations. However, there is a lack of experimental study that shows the feasibility and practical performance limits of this approach under real noise and interference sources in wireless channels. In this work, we implemented energy detector on a wireless testbed and measured the required sensing time needed to achieve the desired probability of detection and false alarm for modulated and sinewave-pilot signals in low SNR regime. We measured the minimum detectable signal levels set by the receiver noise uncertainties. Our experimental study also measured the sensing improvements achieved via network cooperation, identified the robust threshold rule for hard decision combining and quantified the effects of spatial separation between radios in indoor environments.

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