Fast Compressed Power Spectrum Estimation: Toward a Practical Solution for Wideband Spectrum Sensing

Linxiao Yang, Jun Fang, Huiping Duan, Hongbin Li

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

There has been a growing interest in wideband spectrum sensing due to its applications in cognitive radios and electronic surveillance. To overcome the sampling rate bottleneck for wideband spectrum sensing, in this paper, we study the problem of compressed power spectrum estimation whose objective is to reconstruct the power spectrum of a wide-sense stationary signal based on sub-Nyquist samples. By exploring the sampling structure inherent in the multicoset sampling scheme, we develop a computationally efficient method for power spectrum reconstruction. An important advantage of our proposed method over existing compressed power spectrum estimation methods is that our proposed method, whose primary computational task consists of fast Fourier transform (FFT), has a very low computational complexity. Such a merit makes it possible to efficiently implement the proposed algorithm in a practical field-programmable gate array (FPGA)-based system for real-time wideband spectrum sensing. Our proposed method also provides a new perspective on the power spectrum recovery condition, which leads to a result similar to what was reported in prior works. Simulation results are presented to show the computational efficiency and the effectiveness of the proposed method.

Original languageEnglish
Article number8874996
Pages (from-to)520-532
Number of pages13
JournalIEEE Transactions on Wireless Communications
Volume19
Issue number1
DOIs
StatePublished - Jan 2020

Keywords

  • Compressed sampling
  • power spectrum estimation
  • wideband spectrum sensing

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