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Bingxuan ZHAO Wireless Communication and Satellite Communication Project II Shimamoto Laboratory, GITS zhaobx@fuji.waseda.jp Waseda University Ph.D Academy Spectrum Sensing for Wireless Networks 1
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Outline Introduction Cooperative Spectrum Sensing Conclusion of the Dissertation 2
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Current Status of Wireless Spectrum Limited Supply vs. Growing Demand http://www.lbl.gov/MicroWorlds/ALSTool/EMSpec/EMSpec2.html 3
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Current Status of Wireless Spectrum Scarcity vs. Largely Underutilized http://en.wikipedia.org/wiki/Frequency_allocation 4 Cognitive Radio: improve spectrum utilization http://www.its.bldrdoc.gov/isart/art06/slides06/ mch_m/mch_m_slides.pdf
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Research Question Cooperative Spectrum Sensing Effectively find the White Spaces: decrease P FA Avoid interference with the PUs: decrease P MD How to address the power uncertainty problem to decrease P FA and P MD 5 Noise Power Uncertainty
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Local Sensing Techniques 6 Simplicity No prior-knowledge required Most widely used Energy Detection mechanism Time Domain Frequency Domain
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Cooperative Spectrum Sensing 7 Data Collocation Data Processing Data Reporting Infer Presence Absence Soft Combing: Data fusion, high performance, high BW requirement Hard Combing: Decision fusion, low performance, low BW requirement
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Model of Inter-Channel Interference 8 Superposed Power
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Power Decomposition 1/3 9 d is the distance between tx and r. P t is the transmission power beta is the path loss exponent I(u,v) is the interference factor is constant The received power of the receiver, r, working on channel u produced by the transmitter, tx, working on channel v can be represented by: ACM Sigmetrics (2006)
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Power Decomposition 2/3 10 The total received power by a secondary user s(i,j): Mathematical Transform
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Performance Evaluation of Power Decomposition 11 Power decomposition works well in low SINR with conventional method can not. Soft combination can achieve better performance than hard combination. Power decomposition can cope with the increase of the inter-channel interference. Power decomposition can achieve lower P FA, i.e., higher spectrum utilization. Power decomposition can achieve higher P D, lower interference with PUs
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Conclusion of Chapter 3 Proposed a power decomposition method: Non-coherent: depends only on distances Improve spectrum utilization Decrease interference with PUs 12
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