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Unformatted text preview: ECE 5510: Random Processes Lecture Notes Fall 2008 Lecture 20 Today: (0) Gaussian R.P.s (1) Power Spectral Density • AA #5. Discussion items (2). • HW 9 due on Tuesday Nov 25 at 5pm. • Reading: We’ve finished covering Ch 10 (all sections), moving on to Ch 11. Please read 11.1-.3, 11.6, and 11.8. • OH today from 12:30-2. I need to leave at 2pm. 1 Power Spectral Density of a WSS Signal Now we make specific our talk of the spectral characteristics of a random signal. What would happen if we looked at our WSS random signal on a spectrum analyzer? (That is, set it to average) Def’n: Fourier Transform Functions g ( t ) and G ( f ) are a Fourier transform pair if G ( f ) = integraldisplay ∞ t =-∞ g ( t ) e- j 2 πft dt g ( t ) = integraldisplay ∞ f =-∞ G ( f ) e j 2 πft df PLEASE SEE TABLE 11.1 ON PAGE 413. We don’t directly look at the FT on the spectrum analyzer; we look at the power in the FT. Power for a complex value G is S = | G | 2 = G * · G ....
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- Fall '08
- spectral density, Autocorrelation, Stationary process, Sx, Wiener–Khinchin theorem