1. 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

2. Outline Introduction Cooperative Spectrum Sensing Conclusion of the Dissertation 2

3. Current Status of Wireless Spectrum Limited Supply vs. Growing Demand http://www.lbl.gov/MicroWorlds/ALSTool/EMSpec/EMSpec2.html 3

4. 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

5. 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

6. Local Sensing Techniques 6 Simplicity No prior-knowledge required Most widely used Energy Detection mechanism Time Domain Frequency Domain

7. 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

8. Model of Inter-Channel Interference 8 Superposed Power

9. 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)

10. Power Decomposition 2/3 10 The total received power by a secondary user s(i,j): Mathematical Transform

11. 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

12. Conclusion of Chapter 3 Proposed a power decomposition method: Non-coherent: depends only on distances Improve spectrum utilization Decrease interference with PUs 12