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e<br />

n mn<br />

mˆ<br />

n<br />

is<br />

a<br />

mˆ<br />

n<br />

The quantizer output is<br />

e<br />

q<br />

n en<br />

qn<br />

qnis<br />

where<br />

The prediction filter input is<br />

m<br />

q<br />

<br />

n mˆ<br />

n<br />

en qn<br />

m<br />

q<br />

Processing Gain:<br />

2<br />

where <br />

2<br />

where <br />

prediction<br />

From (3.74)<br />

The (SNR)<br />

(SNR)<br />

M<br />

(SNR)<br />

E<br />

(SNR)<br />

Processing Gain,<br />

value.<br />

(3.74)<br />

(3.75)<br />

quantizati on error.<br />

(3.77)<br />

mn<br />

n mn<br />

qn (3.78)<br />

of the DPCM systemis<br />

2<br />

and <br />

o<br />

o<br />

o<br />

2<br />

M<br />

2<br />

Q<br />

2<br />

<br />

M<br />

(<br />

2<br />

<br />

G<br />

2<br />

<br />

E<br />

)( )<br />

2<br />

<br />

(SNR)<br />

G<br />

(3.79)<br />

(3.80)<br />

Design a prediction filter to maximize<br />

<br />

is the variance of the predictions error<br />

and the signal - to - quantizati on noise ratio is<br />

Q<br />

<br />

<br />

<br />

p<br />

Q<br />

are variances of m n<br />

E<br />

<br />

<br />

<br />

2<br />

E<br />

2<br />

Q<br />

p<br />

Q<br />

Q<br />

(3.81)<br />

<br />

(3.82)<br />

<br />

2<br />

M<br />

2<br />

E<br />

( E[<br />

m[<br />

n]]<br />

0) and q<br />

G<br />

p<br />

2<br />

(minimize )<br />

E<br />

n

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