1 / 12

Spectrum Sensing for Wireless Networks

Waseda University Ph.D Academy. Spectrum Sensing for Wireless Networks. Bingxuan ZHAO Wireless Communication and Satellite Communication Project II Shimamoto Laboratory, GITS zhaobx@fuji.waseda.jp. Outline. Introduction Cooperative Spectrum Sensing Conclusion of the Dissertation.

lona
Download Presentation

Spectrum Sensing for Wireless Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Waseda University Ph.D Academy Spectrum Sensing for Wireless Networks Bingxuan ZHAO Wireless Communication and Satellite Communication Project II Shimamoto Laboratory, GITS zhaobx@fuji.waseda.jp

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

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

  4. Current Status of Wireless Spectrum http://en.wikipedia.org/wiki/Frequency_allocation http://www.its.bldrdoc.gov/isart/art06/slides06/mch_m/mch_m_slides.pdf • Cognitive Radio: improve spectrum utilization Scarcity vs. Largely Underutilized 4

  5. Research Question Cooperative Spectrum Sensing • Effectively find the White Spaces: decrease PFA • Avoid interference with the PUs: decrease PMD Noise Power Uncertainty • How to address the power uncertainty problem to decrease PFA and PMD

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

  7. Cooperative Spectrum Sensing Presence Data Processing Data Collocation Data Reporting Infer Absence • Soft Combing: Data fusion, high performance, high BW requirement • Hard Combing: Decision fusion, low performance, low BW requirement 7

  8. Model of Inter-Channel Interference Superposed Power

  9. Power Decomposition 1/3 • The received power of the receiver, r,working on channel u produced by the transmitter, tx, working on channel v can be represented by: • d is the distance between tx and r. • Pt is the transmission power • beta is the path loss exponent • I(u,v) is the interference factor • is constant ACM Sigmetrics (2006)

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

  11. Performance Evaluation of Power Decomposition • 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 PFA, i.e., higher spectrum utilization. • Power decomposition can achieve higher PD, 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 Thank you very much for your kind attention !

More Related