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B–7 Examples of Important Distributions 703<br />

1.0<br />

0.5<br />

10 1<br />

1<br />

2pz<br />

e z2 /2<br />

10 2<br />

Q (z)<br />

10 3<br />

10 4<br />

10 5<br />

10 6<br />

10 7<br />

10 8<br />

0<br />

Figure B–7<br />

Sinusoidal Distribution<br />

1 2 3 4 5 6<br />

The function Q(z) and an overbound,<br />

z<br />

1<br />

12p z e-z2 >2 .<br />

THEOREM. If x = A sin c, where y has the uniform distribution,<br />

f c (c) = L<br />

1<br />

2p ,<br />

then the PDF for the sinusoid is given by<br />

|c| … p<br />

0, elsewhere<br />

0, x 6 -A<br />

1<br />

f x (x) = e<br />

p2A 2 - x , |x| … A<br />

2<br />

(B–66)<br />

(B–67)<br />

0, x 7 A<br />

A proof of this theorem will be given in Sec. B–8.<br />

A sketch of the PDF for a sinusoid is given in Table B–1, along with equations for the<br />

CDF and the variance. Note that the standard deviation, which is equivalent to the RMS value<br />

as discussed in Chapter 6, is s = A> 12. This should not be a surprising result.<br />

The sinusoidal distribution can be used to model observed phenomena. For example,<br />

x might represent an oscillator voltage where c = v 0 t + u 0 . Here the frequency of oscillation<br />

is f 0 , and ω 0 and t are assumed to be deterministic values. u 0 represents the random<br />

startup phase of the oscillator. (When the power of an unsynchronized oscillator is turned on,

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