1. Blind-Spectrum Non-uniform Sampling and its Application in
Wideband Spectrum Sensing By. M. R. Avendi
2. Agenda Blind Spectrum Signal Model Parameters L, p, q, C
Spectral Recovery Subspace Method NLLS Method Simulation
Application for Spectrum Sensing Application for Spectrum Sensing
Cognitive Overview Spectrum sensing Current methods Proposed model
Simulation Summary and conclusion 2
3. Blind spectrum signal model Number of bands N Each band no
wider than B Maximum frequency fmax Locations unknown Landau lower
bound (F)=NB 3
4. Sampling Parameters Number of active slots : qmin < q
< qmax q=3 4 Minimum and Maximum number of active slots q=6
5. Sampling Parameters L p > qmax q = N(d+1) 5 qmax= N(d+1)
Sampling Rate
6. Sampling Parameters Sample pattern C - Exhaustive Search-
Exhaustive Search - Random Selection - Sequential Search 6
7. Spectral Recovery Ideal model y(f)=AC(k) z(f) Non-ideal
model y(f)= A (k) z(f)+ n(f)y(f)= AC(k) z(f)+ n(f) y is known, k
and z are unknown n(f) additive white noise spectral index set k ?
7
8. Spectral Recovery Subspace method 8
9. Number of active slots q 9 Ordered Eigenvalues
10. Number of active slots Information theoretic criteria
approaches AIC : Akaike Information criterion MDL: Minimum
Description length 10
11. Number of active slots Exponential fitting test (EFT) 11
Ordered Eigenvalues of signal and noise
12. Location of active slots MUSIC algorithm 12 The k-th column
of modulation matrix
13. MUSIC-Algorithm 13 Signal spectrum and MUSIC- Results
26. simulation: 26 Detection probability vs. SNR for various
Compression Ratio
27. Summary & Conclusion Periodic non-uniform Sampling
& Reconstruction Sampling parameters: L, p ,sample pattern C
Spectral recovery : Subspace, NLLS Wideband spectrum sensing Future
works: Implementation aspects of non-uniform ADC General sample
pattern for blind spectrum signal Reduce the sample rate for
extreme case of blind signals Formulation of detection probability
vs. SNR and CR 27
