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CR Wideband Spectrum Sensing Baseband Progress Report 05/29/2009

CR Wideband Spectrum Sensing Baseband Progress Report 05/29/2009. Tsung-Han Yu thyu0918@ee.ucla.edu. Outline. Motivation System specification Weak signal detection Sideband power estimation Conclusion. Motivation. Why cognitive radio Growing demand on spectrum utilization

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CR Wideband Spectrum Sensing Baseband Progress Report 05/29/2009

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  1. CR Wideband Spectrum Sensing Baseband Progress Report05/29/2009 Tsung-Han Yu thyu0918@ee.ucla.edu

  2. Outline • Motivation • System specification • Weak signal detection • Sideband power estimation • Conclusion

  3. Motivation • Why cognitive radio • Growing demand on spectrum utilization • Opportunistic way to access spectrum • Spectrum sensing • Key function for cognitive radio system • Reliably detect weak primary signals • Avoid harmful interference • Why wideband sensing • Sense empty channel at a time • Save RF front-end design complexity • Challenge • Channelize signal introduce spectral leakage

  4. System Specification • Weak signal detection • Wideband sensing ~ 250MHz • Signal detection < -5dB • PFA < 0.1 / PD > 0.9 • Spectral resolution ~ 200 KHz • Sideband power estimation • Measure reference sideband power down to -70 dBm • Estimation error < 0.5 dB • Sensing time < 20-30 ms

  5. Weak Signal Detection • PSD-based energy detection • Apply FFT for spectrum estimation • Apply polyphase filterbank to reduce spectral leakage

  6. Polyphase Filterbank • Apply polyphase filterbank [1], [2] • Decompose the M-tap lowpass filter into N K-tap lowpass filter

  7. Spectrum Sensing in Freq. Domain • Power Detector: • H0: • Mean • Variance • H1: • Mean • Variance N: FFT size M: # of Avg. K: BW (in FFT bin)

  8. Numerical Result (1/2) • Large-bandwidth signal detection • ~8 us sensing time for -5dB SNR • Large-bandwidth signal detection with strong blockers

  9. Numerical Result (2/2) • Narrow-bandwidth signal detection • ~0.4 ms sensing time for -5dB SNR • Narrow-bandwidth signal detection with strong blockers • Interferer cancellation

  10. Computation Complexity

  11. Sideband Power Estimation • Use energy detector to measure sideband power • Apply FFT to channelize the spectrum • Apply polyphase filterbank to reduce spectral leakage Force the sideband at a FFT bin Avg. the bin power Measure the bin power

  12. Summary • Sensing large bandwidth signal • w/o strong blocker • Take ~8us (2 FFT avg.) sensing time • w/ strong blocker • Take ~8us (2 FFT avg.) sensing time • Improve PMD by 3X (PFA ~ 0.1) • Sensing narrow bandwidth signal • w/o strong blocker • Take ~0.4ms (100 FFT avg.) sensing time • w/ strong blocker • Require more sensing time • Require interferer cancellation

  13. Future Work • Baseband algorithm • Computation complexity analysis • Interference cancellation • Match-filter-based spectrum sensor • Match-filter-based sideband power estimator • Baseband implementation • Low-power high speed FFT design • BEE2 platform for real-time emulation

  14. Reference • [1] B Farhang-Boroujeny, Filter Bank Spectrum Sensing for Cognitive Radios, IEEE Trans. Signal Processing, vol. 56, no. 5, May 2008, pp. 1801-1811. • [2] F. Sheikh, and B Bing, Cognitive Spectrum Sensing and Detection Using Polyphase DFT Filter Banks, in Proc. 5th IEEE Consumer Communications and Networking Conference (CCNC), Jan. 2008, pp. 973-977.

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