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The message signal and additive interferer with magnitude spectra shown in [link] are applied to the system in [link] . The analog mixer frequencies are kHz and kHz. The BPF with output is assumed ideal, is centered at , has lower cutoff frequency , upper cutoff frequency , and zero phase at . The period of the sampler is seconds. The phase of the discrete-time mixer is assumed to be adjusted to the value that maximizes the ratioof signal to interferer noise power in . The LPF with output is assumed ideal with cutoff frequency . The design objective is for the spectrum of to estimate the spectrum of a sampled . You are to select the upper and lower cutoff frequencies of theBPF, the frequency of the discrete-time mixer, and the cutoff frequency of the LPF in order to meet this objective.
Consider the digital receiver in [link] producing , which is intended to match the input sampled every seconds. The absolute bandwidth of is . The carrier frequency is 10 times . The sample frequency is 2.5 times . Note that the sample frequency is above the Nyquist frequency of the received signal . Determine the maximum cutoff frequency as a function of theinput bandwidth for the lowpass filter producing so the design objective of matching samples of with a sample frequency of is achieved.
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:
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 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 second of a 100 Hz sine wave with a sampling rate of 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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