Quickest Sequential Multiband Spectrum Sensing With Mixed Observations

Jun Geng, Weiyu Xu, Lifeng Lai

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Spectrum sensing is a key technology enabling the cognitive radio system. In this paper, the problem of how to quickly and accurately find an unoccupied channel from a large amount of potential channels is considered. The cognitive radio system under consideration is equipped with a narrow band sensor, hence it can only sense those potential channels in a sequential manner. In this scenario, we propose a novel two-stage mixed-observation sensing strategy. In the first stage, which is named as scanning stage, the sensor observes a linear combination of the signals from a pair of channels. The purpose of the scanning stage is to quickly identify a pair of channels such that at least one of them is highly likely to be unoccupied. In the second stage, which is called refinement stage, the sensor only observers the signal from one of those two channels identified from the first stage, and selects one of them as the unoccupied channel. The problem under this setup is an ordered two concatenated Markov stopping time problem. The optimal solution is solved using the tools from the multiple stopping time theory. It turns out that the optimal solution has a rather complex structure, hence a low complexity algorithm is proposed to facilitate the implementation. In the proposed low complexity algorithm, the cumulative sum test is adopted in the scanning stage and the sequential probability ratio test is adopted in the refinement stage. The performance of this low complexity algorithm is analyzed when the presence of unoccupied channels is rare. Numerical simulation results show that the proposed sensing strategy can significantly reduce the sensing time when the majority of potential channels are occupied.
Original languageEnglish (US)
Pages (from-to)5861-5874
Number of pages14
JournalIEEE Transactions on Signal Processing
Issue number22
StatePublished - Nov 15 2016
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2021-04-02
Acknowledged KAUST grant number(s): OCRF-2014-CRG-3
Acknowledgements: The work of J. Geng was supported by the Fundamental Research Funds for the Central Universities under Grant AUGA5710013915. The work ofW. Xu was supported by the Simons Foundation, KAUST OCRF-2014-CRG-3, NSF DMS-1418737 and NIH 1R01EB020665-01. The work of L. Lai was supported by the National Science Foundation under Grant CNS-16-60128. The results in this paper were presented in part at IEEE International Symposium on Information Theory, Istanbul, Turkey, July, 2013.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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