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The message signal u ( t ) and additive interferer w ( t ) with magnitude spectra shown in [link] are applied to the system in [link] . The analog mixer frequencies are f c = 1600 kHz and f d = 1240 kHz. The BPF with output x ( t ) is assumed ideal, is centered at f c , has lower cutoff frequency f L , upper cutoff frequency f U , and zero phase at f c . The period of the sampler is T s = 1 71 × 10 - 4 seconds. The phase β of the discrete-time mixer is assumed to be adjusted to the value that maximizes the ratioof signal to interferer noise power in y ( k T s ) . The LPF with output y ( k T s ) is assumed ideal with cutoff frequency γ . The design objective is for the spectrum of y ( k T s ) to estimate the spectrum of a sampled u ( t ) . You are to select the upper and lower cutoff frequencies of theBPF, the frequency α of the discrete-time mixer, and the cutoff frequency of the LPF in order to meet this objective.

  1. Design the desired BPF by specifying its upper f U and lower f L cutoff frequencies.
  2. Compute the desired discrete-time mixer frequency α .
  3. Design the desired LPF by specifying its cutoff frequency γ .
Magnitude spectra of the message and interference signals considered in Exercise 6-12.
Magnitude spectra of the message and interference signals considered in Exercise [link] .
Schematic of the digital receiver used in Exercise 6-12.
Schematic of the digital receiver used in Exercise [link] .

Consider the digital receiver in [link] producing y ( k T s ) , which is intended to match the input x ( t ) sampled every T s seconds. The absolute bandwidth of x ( t ) is B . The carrier frequency f c is 10 times B . The sample frequency 1 / T s is 2.5 times f c . Note that the sample frequency 1 / T s is above the Nyquist frequency of the received signal r ( t ) . Determine the maximum cutoff frequency as a function of theinput bandwidth B for the lowpass filter producing y ( k T s ) so the design objective of matching samples of x ( t ) with a sample frequency of 1 / T s is achieved.

Digital receiver considered in Exercise 6-13.
Digital receiver considered in Exercise [link] .

Exploring sampling in matlab

It is not possible to capture all of the complexities of analog-to-digital conversion inside a computer program,because all signals within a (digital) computer are already “sampled.” Nonetheless, most of the key ideas can be illustrated byusing two tricks to simulate the sampling process:

  • Evaluate a function at appropriate values (or times).
  • Represent a data waveform by a large number of samples and then reduce the number of samples.

The first is useful when the signal can be described by a known function, while the second is necessary whenever theprocedure is data driven, that is, when no functional form is available. This section explores both approachesvia a series of M atlab experiments.

Consider representing a sine wave of frequency f = 100  Hz. The sampling theorem asserts that the sampling rate must be greater than the Nyquist rate of 200 samples per second.But in order to visualize the wave clearly, it is often useful to sample considerably faster. The following M atlab code calculates and plots the first 1 / 10 second of a 100 Hz sine wave with a sampling rate of f s = 1 T s = 10000 samples per second.

f=100;                       % frequency of wave time=0.1;                    % total time in secondsTs=1/10000;                  % sampling interval t=Ts:Ts:time;                % define a "time" vectorw=sin(2*pi*f*t);             % define the sine wave plot(t,w)                    % plot the sine vs. timexlabel('seconds') ylabel('amplitude')
sine100hz.m generate 100 Hz sine wave with sampling rate fs=1/Ts (download file)

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Source:  OpenStax, Software receiver design. OpenStax CNX. Aug 13, 2013 Download for free at http://cnx.org/content/col11510/1.3
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