Joint Detection Algorithm for Spectrum Sensing Over Multipath Time-Variant Flat Fading Channels

  • Mengwei Sun
  • Yan Zhang
  • Long Zhao
  • Bin Li
  • Chenglin Zhao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)


A popular approach to spectrum sensing is matched filter which could achieve the optimum performance in short sensing time. Under multipath time-variant flat fading channel, this conventional spectrum sensing method could be cumbersome to implement due to channel variation. This paper puts forward a new spectrum sensing algorithm in allusion to this defect. We firstly propose a dynamic state-space model which could thoroughly characterize the evolution of two hidden states: primary user state and the multipath fading channel. Then a promising joint estimation algorithm of these two states based on maximum a posteriori probability criteria and particle filtering technology is presented. Experimental simulations are provided to demonstrate the superior performance of our presented joint detection scheme.


Spectrum sensing Multipath time-variant flat fading channel Dynamic state-space model Joint estimation Particle filtering 



This work was supported by the National Natural Science Foundation of China (61271180), Major National Science and Technology Projects (2012zx03001022) and Special Foundation for State Internet of Things Program (Radio frequency and communication security testing service platform of Internet of things).


  1. 1.
    Mitola J, Maguire GQ (1999) Cognitive radio: making software radios more personal. Personal Communications. IEEE 6(4):13–18Google Scholar
  2. 2.
    Digham FF, Alouini MS, Simon MK (2003) On the energy detection of unknown signals over fading channels. Proc. of IEEE international conference on communications, vol 5, Anchorage, AK, May 2003, pp 3575–3579Google Scholar
  3. 3.
    Zhang Z, Yang Q, Wang L, Zhou X (2010) A novel hybrid matched filter structure for IEEE 802.22 standard. Circuit Syst (APCCAS), IEEE Asia Pacific conference, 2010, pp 652–655Google Scholar
  4. 4.
    Cabric D, Mishra SM, Brodersen RW (2004) Implementation issues in spectrum sensing for cognitive radio. Proc. Asilomar Conf. Signals, Syst., Comput., vol 1, Nov 2004, pp 772–776Google Scholar
  5. 5.
    Djuric PM, Kotecha JH, Zhang JQ, Huang YF, Chirmai T, Bugallo MF, Miguez J (2003) Particle filtering. IEEE Signal Process Magazine 20(5):19–38CrossRefGoogle Scholar
  6. 6.
    Sadeghi P, Kennedy R, Rapajic P, Shams R (2008) Finite-state Markov modeling of fading channels: a survey of principles and applications. IEEE Signal Process Magazine 25(5):57–80CrossRefGoogle Scholar
  7. 7.
    Zhang D, Tian Z (2007) Adaptive game-based radio spectrum allocation in doubly selective fading channels. IEEE international conference on digital object identifier, 2007, pp 5172–5176Google Scholar
  8. 8.
    Miguez J, Djuric PM (2004) Blind equalization of frequency-selective channels by sequential importance sampling. IEEE Trans Signal Process 52(10):2738–2748CrossRefMathSciNetGoogle Scholar
  9. 9.
    Liu JS, Chen R (1995) Blind deconvolution via sequential imputations. J Am Stat Assoc 90(430):567–576CrossRefMATHGoogle Scholar
  10. 10.
    Doucet A (2000) On sequential Monte Carlo sampling methods for Bayesian filtering. Stat Comput 10:197–208CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mengwei Sun
    • 1
  • Yan Zhang
    • 1
  • Long Zhao
    • 1
  • Bin Li
    • 1
  • Chenglin Zhao
    • 1
  1. 1.Key Lab of Universal Wireless CommunicationsBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations