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[2] Delay the signal by say 32 samples and reduce its amplitude by factor of say α = 0.7, call this xd(n) as shown below: xd(n) 5α n=34 N=25

[2] Delay the signal by say 32 samples and reduce its amplitude by factor of say α = 0.7, call this xd(n) as shown below: xd(n) 5α n=34 N=25

Introductory Circuit Analysis (13th Edition)
Introductory Circuit Analysis (13th Edition)
13th Edition
ISBN:9780133923605
Author:Robert L. Boylestad
Publisher:Robert L. Boylestad
Chapter1: Introduction
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[1] [2] [3] [4] [5] [6] Generate a single pulse for the transmitted signal as shown b x(n) 5 0 n=4 Delay the signal by say 32 samples and reduce its amplitude by factor of say a = 0.7, call this xd(n) as shown below: xd(n) 5α 0 n=34 N=25 Generate N=256 samples of Gaussian random signal and call t Generate the simulated received signal by adding the transmitt and the noise signal w(n), i.e. r(n) = ax(n-d) + sigman x w(n) Where sigman is the noise amplitude (initially set this using subplot(2,2,1), plot the signals x(n), xd(n), and r(n). A and grid the each plot. Estimate the cross-correlation sequence R.(m) and plot in quadrant of the figure. Note, plot only half the samples of t correlation sequence returned by the function xcorr. This ca follows: % Assuming there are N samples of x Y=xcorr(r,x); R=Y(1:N); Rrx-fliplr(R);
Transcribed Image Text:[1] [2] [3] [4] [5] [6] Generate a single pulse for the transmitted signal as shown b x(n) 5 0 n=4 Delay the signal by say 32 samples and reduce its amplitude by factor of say a = 0.7, call this xd(n) as shown below: xd(n) 5α 0 n=34 N=25 Generate N=256 samples of Gaussian random signal and call t Generate the simulated received signal by adding the transmitt and the noise signal w(n), i.e. r(n) = ax(n-d) + sigman x w(n) Where sigman is the noise amplitude (initially set this using subplot(2,2,1), plot the signals x(n), xd(n), and r(n). A and grid the each plot. Estimate the cross-correlation sequence R.(m) and plot in quadrant of the figure. Note, plot only half the samples of t correlation sequence returned by the function xcorr. This ca follows: % Assuming there are N samples of x Y=xcorr(r,x); R=Y(1:N); Rrx-fliplr(R);
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