Sparse spectrum sensing with sub-block partition for cognitive radio systems

Meng Lin Ku, Xun Ru Yin

Research output: Contribution to journalConference articlepeer-review


Cognitive users are expected to be capable of exploring spectrum holes over a wide range of frequencies. Motivated by the sparse characteristic of underutilized spectrum, we consider sparse spectrum sensing using compressive sensing techniques for cognitive orthogonal frequency division multiplexing (OFDM) systems. The spectrum sensing problem is formulated as a multi-subcarrier detection problem, solved via the composite hypothesis testing and Neyman-Pearson criterion. Considering the availability of channel state information (CSI) at the cognitive device, two sparse spectrum sensing approaches are proposed for detecting the compressive received signals in time domain. For the purpose of complexity reduction, we further incorporate a sub-block partition scheme into the proposed approaches to leverage the spareness of the spectrum occupancy. The proposed approaches enable a flexible tradeoff between the implementation complexity and the sensing accuracy for wideband cognitive radios.

Original languageEnglish
Article number7023104
JournalIEEE Vehicular Technology Conference
Issue numberJanuary
StatePublished - 2014
Event2014 79th IEEE Vehicular Technology Conference, VTC 2014-Spring - Seoul, Korea, Republic of
Duration: 18 May 201421 May 2014


  • Cognitive radio
  • Compressive sensing
  • Spectrum sensing


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