A recursive algorithm for joint time-frequency wideband spectrum sensing

Abstract—In wideband spectrum sensing, secondary or un-licensed users take signal measurements over a given wide spectrum band and attempt to determine subbands for which the spectrum is idle and thus available for use. Some recent approaches to finding such spectrum holes generally employ some form of edge detection or energy detection. We propose an algorithm for joint time-frequency wideband spectrum sensing based on applying a form of temporal spectrum sensing together with a recursive tree search. The algorithm is able to detect spec-trum holes accurately even in the presence of bursting primary signals and primary signals whose power spectral densities have smooth band edges. Numerical results are presented which show the performance gain of the proposed algorithm over earlier approaches to wideband spectrum sensing.1 Index Terms—Cognitive radio, spectrum sensing, dynamic spectrum access I

Sub-Nyquist Sampling: Bridging Theory and Practice

Sampling theory encompasses all aspects related to the conversion of continuous-time signals to discrete streams of numbers. The famous Shannon-Nyquist theorem has become a landmark in the development of digital signal processing. In modern applications, an increasingly number of functions is being pushed forward to sophisticated software algorithms, leaving only those delicate finely-tuned tasks for the circuit level. In this paper, we review sampling strategies which target reduction of the ADC rate below Nyquist. Our survey covers classic works from the early 50's of the previous century through recent publications from the past several years. The prime focus is bridging theory and practice, that is to pinpoint the potential of sub-Nyquist strategies to emerge from the math to the hardware. In that spirit, we integrate contemporary theoretical viewpoints, which study signal modeling in a union of subspaces, together with a taste of practical aspects, namely how the avant-garde modalities boil down to concrete signal processing systems. Our hope is that this presentation style will attract the interest of both researchers and engineers in the hope of promoting the sub-Nyquist premise into practical applications, and encouraging further research into this exciting new frontier.Comment: 48 pages, 18 figures, to appear in IEEE Signal Processing Magazin

Impact of Noise Power Uncertainty on the Performance of Wideband Spectrum Segmentation

The objective of this work is to investigate the impact of noise uncertainty on the performance of a wideband spectrum segmentation technique. We define metrics to quantify the degradation due to noise uncertainty and evaluate the performance using simulations. Our simulation results show that the noise uncertainty has detrimental effects especially for low SNR users