Cooperative spectrum sensing for cognitive radios using kriged kalman filtering

Seung Jun Kim, Emiliano Dall'Anese, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

114 Scopus citations


A cooperative cognitive radio (CR) sensing problem is considered, where a number of CRs collaboratively detect the presence of primary users (PUs) by exploiting the novel notion of channel gain (CG) maps. The CG maps capture the propagation medium per frequency from any point in space and time to each CR user. They are updated in real-time using Kriged Kalman filtering (KKF), a tool with well-appreciated merits in geostatistics. In addition, the CG maps enable tracking the transmit-power and location of an unknown number of PUs, via a sparse regression technique. The latter exploits the sparsity inherent to the PU activities in a geographical area, using an ℓ1-norm regularized, sparsity-promoting weighted least-squares formulation. The resulting sparsity-cognizant tracker is developed in both centralized and distributed formats, to reduce computational complexity and memory requirements of a batch alternative. Numerical tests demonstrate considerable performance gains achieved by the proposed algorithms.

Original languageEnglish (US)
Article number5484600
Pages (from-to)24-36
Number of pages13
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number1
StatePublished - Feb 1 2011


  • Channel estimation
  • Kalman filters
  • cognitive radio
  • compressed sampling
  • distributed algorithms

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