Joint Reporting-Fusion Optimization in
Cooperative Spectrum Sensing for Cognitive
Radio Networks
Younes Abdi and Tapani Ristaniemi
Faculty of Information Technology
University of Jyväskylä
Email: [email protected] and [email protected]
Outline
About the Speaker
Current Stage of the Studies and Research
Introduction
System Model
Problem Formulation
Numerical Results
Conclusion
References
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About the Speaker
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Current Stage of the Studies and Research
51 credits have been obtained so far (soon it will turn into 55)
Wonderful once-in-a-lifetime experiences in GETA, EIT ICT Labs,
INFORTE, DELTA, etc.
Extensive self-studies in
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Stochastic Processes,
Markov Chains,
Optimization Theory,
Advanced Communications Theory,
Estimation and Detection Theory
Three publications, and several others are under construction
2014 IET Premium Paper Award
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The (Apparently) Crowded Spectrum
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The White Space Concept
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Cognitive Radio
Next step evolution of Software-Defined Radio (SDR)
Cognitive Radio (CR): Intelligent devices that can
– Sense and autonomously reason about their
environment
– Adapt their communication parameters accordingly
– Realize the DSA concept
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Cooperative Spectrum Sensing
The Hidden Node Problem
Fig. 1. Illustration of the hidden node problem [3]
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Cooperative Spectrum Sensing
Basic configuration: The Listening and Reporting channels, Fusion
Center (FC), Primary and Secondary Users (PU & SUs)
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Cooperative Spectrum Sensing
Distributed detection and data fusion techniques
– Spectrum sensing by the sensor nodes (local sensing)
– Reporting the local sensing outcomes
– Data/decision fusion
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System Model
Proposed Architecture: Linear Fusion of Quantized Reports
Y. Abdi and T. Ristaniemi, “Joint local quantization and linear cooperation in spectrum sensing for cognitive radio
networks,” IEEE Transactions on Signal Processing, vol. 62, no. 17, Sept. 1, 2014
Y. Abdi and T. Ristaniemi, “Extension of deflection coefficient for linear fusion of quantized reports in cooperative
sensing,” in Proc. IEEE PIMRC 2014
Y. Abdi and T. Ristaniemi, “Joint reporting and linear fusion optimization in collaborative spectrum sensing for
cognitive radio networks,” in Proc. 9th International Conference on Information, Communications and Signal
Processing (ICICS 2013), Tainan, Taiwan, December 10-13, 2013 (Invited paper).
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System Model (cont.)
Local sensing and quantization
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System Model (cont.)
Distributions of quantized sensing outcomes and received
sensing outcomes
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System Model (cont.)
Mapping and bit sequences
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System Model (cont.)
Distribution of received bit sequences
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System Model (cont.)
Reporting channel
Linear combining
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Problem Formulation
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Problem Formulation (cont.)
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Joint Reporting-Fusion Optimization
KKT conditions for a given d
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Joint Reporting-Fusion Optimization
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Extended Deflection Coefficient (EDC)
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EDC-Based Optimization
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Numerical Results
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Numerical Results
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Conclusion
In this research, after a structured study of major phases in a centralized
cooperative sensing scheme, the effect of the number of bits used in
local sensing quantization on the overall sensing performance in a CRN
with cooperative sensing has been introduced and a joint optimization
approach has been proposed to optimize the linear soft-combining
scheme at the fusion phase with the number of quantization bits used by
each sensing node at the reporting phase. The presented analytical
expressions followed by simulation results demonstrate that, through
joint consideration of the reporting and fusion phases in a cooperative
sensing scheme, considerable performance gains can be obtained. This
better performance stems from better exploitation of spatial/user
diversities in CRNs. The proposed joint optimization scheme leads to
more powerful distributed detection performance, especially when the
sensing nodes have to work at low SNR regimes.
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References
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Thank you for your kind attention!
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