Analog to digital cognitive radio

Abstract

Enabling cognitive radio (CR) requires revisiting the traditional task of spectrum sensing with specific and demanding requirements in terms of detection performance, real-time processing, and robustness to noise. Unfortunately, conventional spectrum sensing methods do not satisfy these demands. In particular, the Nyquist rate of signals typically sensed by a CR is prohibitively high so that sampling at this rate necessitates sophisticated and expensive analog to digital converters, which lead to a torrent of samples. Over the past few years, several sampling methods have been proposed that exploit signals' a priori known structure to sample them below Nyquist. In this chapter, we review some of these techniques and tie them to the task of spectrum sensing for CRs. We then show how other spectrum sensing challenges can be tackled in the sub-Nyquist regime. First, to cope with low signal-to-noise ratios, spectrum sensing may be based on second-order statistics recovered from the low rate samples. In particular, cyclostationary detection allows to differentiate between communication signals and stationary noise. Next, CR networks, that perform collaborative low rate spectrum sensing, have been proposed to overcome fading and shadowing channel effects. Last, to enhance CR efficiency, we present joint spectrum sensing and direction of arrival estimation methods from sub-Nyquist samples. These allow to map the temporarily vacant bands both in terms of frequency and space. Throughout this chapter, we highlight the relation between theoretical algorithms and results and their practical implementation. We show hardware simulations performed on a prototype built with off-the-shelf devices, demonstrating the feasibility of sub-Nyquist spectrum sensing in the context of CR.