Aziz, B., Traore, S., Nafkha, A. and Le Guennec, D. (2014) Spectrum Sensing for Cognitive Radio Using Multicoset Sampling. Global Communications Conference, Austin, 8-12 December 2014, 816-821.
https://doi.org/10.1109/GLOCOM.2014.7036909

has been cited by the following article:

• TITLE: Wideband Cognitive Radio Networks Based Compressed Spectrum Sensing: A Survey

  AUTHORS: Mohammed Abo-Zahhad, Sabah M. Ahmed, Mohammed Farrag, Khaled Ali BaAli

  KEYWORDS: Cognitive Radio, Spectrum Sensing, Compressive Sensing, Compressed Measurements, Nyquist Sampling Rate

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.9 No.2, May 31, 2018

  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.