Line 24: Line 24:
 
   <math>y(t)  =  e^{j(\omega_c t + \theta_c)}x(t)\!</math>
 
   <math>y(t)  =  e^{j(\omega_c t + \theta_c)}x(t)\!</math>
  
       <math>      = x(t)\sum^{\infty}_{n = -\infty} \delta(t - nT)\!</math>
+
       <math>      = F(e^{j(\omega_c t + \theta_c)} * x(t)\!</math>
  
       <math>      =\sum^{\infty}_{n = -\infty}x(t)\delta(t - nT)\!</math>
+
       <math>      =\frac{1}{2\pi} F(e^{j(\omega_c t + \theta_c)}) * X(\omega)\!</math>
  
       <math>      =\sum^{\infty}_{n = -\infty}x(nT)\delta(t - nT)\!</math>
+
       <math>      =\frac{1}{2\pi} 2\pi \delta(\omega-\omega_c) * X(\omega)\!</math>
 +
 
 +
      <math>      =X(\omega-\omega_c)\!</math>

Revision as of 16:22, 16 November 2008

Complex Exponential Modulation

Many communication systems rely on the concept of sinusoidal amplitude modulation, in which a complex exponential or a sinusoidal signal, $ c(t)\! $, has its amplitude modulated by the information-bearing signal, $ x(t)\! $. $ x(t)\! $ is the modulating signal, and $ c(t)\! $ is the carrier signal. The modulated signal, $ y(t)\! $, is the product of these two signals:

$ y(t) = x(t)c(t)\! $

An important objective of amplitude modulation is to produce a signal whose frequency range is suitable for transmission over the communication channel that is to be used.

One important for of modulation is when a complex exponential is used as the carrier.

$ c(t) = e^{j(\omega_c t + \theta_c)}\! $

$ \omega_c\! $ is called the carrier frequency, and $ \theta_c\! $ is called the phase of the carrier.

Graphically, this equation looks as follows,

              $ x(t)\! $ ----------> x --------> $ y(t)\! $
                              ^
                              |
                              |
                      $ c(t) = e^{j(\omega_c t + \theta_c)}\! $

Mathematically, we can solve for $ Y(\omega)\! $ as follows,

 $ y(t)  =  e^{j(\omega_c t + \theta_c)}x(t)\! $
     $        = F(e^{j(\omega_c t + \theta_c)} * x(t)\! $
     $        =\frac{1}{2\pi} F(e^{j(\omega_c t + \theta_c)}) * X(\omega)\! $
     $        =\frac{1}{2\pi} 2\pi \delta(\omega-\omega_c) * X(\omega)\! $
     $        =X(\omega-\omega_c)\! $

Alumni Liaison

BSEE 2004, current Ph.D. student researching signal and image processing.

Landis Huffman