In reality, multiple radio stations operate in the same metro area, or *market**.* When you tune in a signal at 750 kHz, another signal may be at 760 kHz. To find out whether the adjacent signal impacts the simple receiver design, assume that the interference is a single tone, *A** _{i}*cos(2

*πf*

_{i}*t*). The received signal is now of the form

with *f** _{i}* assumed to lie just outside the +/– 5-kHz channel bandwidth centered on

*f*

*.*

_{c}Using a generalized version of the phasor addition formula, you can show that the received envelope with single-tone interference is as follows:

Note that

The envelope detector recovers envelope *R*(*t*). To find *R*(*t*) for a new signal model, use the phasor additional formula, which can be shown to hold for time-varying amplitudes and phases of the constituent terms. The enhanced formula states that

where

The key to this formula working is the common *f*_{0} found in each cosine term.

For AM plus single-tone interference, you can make the formula work by adding and subtracting *f** _{c}* in the interference term:

In the formula *A*_{1}(*t*) = *A** _{c}*[1 +

*am*(

*t*)],

Now, calculate

Add these complex numbers in rectangular form and then find the magnitude:

The last line follows from

Because

you can combine the terms and drop the absolute value. As a check, if *A** _{i}* = 0, that is, no interference, the result for

*R*(

*t*) reduces to

The envelope detector is relatively easy to implement in hardware, but it’s a little difficult to analyze. You can explore the model for *R*(*t*) to get a feel for what’s going on. For starters, the input/output relationship is nonlinear, as evidenced by the squares and square root operations. Even with *A*_{i}_{ }= 0, *R*(*t*) contains an absolute value. At this point, assume *m*(*t*) = cos(2*πf*_{m}*t*) as simple test case.

The Python function env_plot(t,Ac,Am,fm,Ai,fi) allows *R*(*t*) to be plotted as well as its spectrum. The spectrum *P** _{R}*(

*f*) is a result of using PyLab’s psd() function.

In [<b>346</b>]: def env_plot(t,Ac,Am,fm,Ai,fi): ...: R = sqrt((Ac+Am*cos(2*pi*fm*t) +Ai*cos(2*pi*dfi*t))**2 +(Ai*sin(2*pi*dfi*t))**2) ..: return R In [<b>347</b>]: t = arange(0,20,1/500.) # T=20ms, fs=500 kHz

Exercise the function by using a time vector running over 20 ms at an effective sampling rate of 500 ksps and then plot time-domain and frequency-domain results side by side (see the 3 x 2 subplot array in the figure).

Set

Also set *A** _{m}* = 0.5, which is equivalent to setting

*a*= 0.5 (50 percent modulation depth). The value of

*A*

*steps over 0, 0.1, and 1.0. The 2-kHz message is within the 5-kHz message bandwidth requirement and*

_{i}kHz for the interference places it in the adjacent channel (5 kHz is the crossover frequency).

Here are the primary IPython command line entries:

In [<b>447</b>]: R = env_plot(t,1,.5,2,0,7) In [<b>449</b>]: plot(t,R) In [<b>454</b>]: psd(R,2**13,500); In [<b>457</b>]: R = env_plot(t,1,.5,2,0.1,7) In [<b>459</b>]: plot(t,R) In [<b>464</b>]: psd(R,2**13,500); In [<b>467</b>]: R = env_plot(t,1,.5,2,1,7) In [<b>469</b>]: plot(t,R) In [<b>475</b>]: psd(R,2**13,500);

The only way to eliminate or reduce the interference is with a BPF in front of the envelope detector. The superheterodyne option is a good choice here, because the BPF doesn’t need to be tunable. At first, you may be disturbed that an out-of-band signal can produce in-band interference, but you should always expect the unexpected from nonlinearities. The upside is that the receiver design is still low cost.