Optimal linear fusion for distributed spectrum sensing via semidefinite programming

  • Authors:

  • Zhi Quan;Wing-Kin Ma;Shuguang Cui;Ali H. Sayed

  • Affiliations:

  • Department of Electrical Engineering, University of California, Los Angeles, 90095, USA;Department of Electronic Engineering, Chinese University of Hong Kong, Shatin, N.T., Hong Kong;Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, USA;Department of Electrical Engineering, University of California, Los Angeles, 90095, USA

  • Venue:
  • ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year:
  • 2009

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Abstract

As an enabling functionality of overlay cognitive radio networks, spectrum sensing needs to reliably detect licensed signal in the band of interest. To achieve reliable sensing, we propose a linear fusion scheme for distributed spectrum sensing to combine the sensing results from multiple spatially distributed cognitive radios. The optimal linear fusion design is formulated into a nonconvex optimization problem. We show that the optimal solution of such a nonconvex problem can be solved via semi-definite programming reformulation.