Why is sampling theorem important in communication?

Why is sampling theorem important in communication?

The sampling theorem specifies the minimum-sampling rate at which a continuous-time signal needs to be uniformly sampled so that the original signal can be completely recovered or reconstructed by these samples alone. This is usually referred to as Shannon’s sampling theorem in the literature.

Why do we need sampling theorem?

If the signal contains high frequency components, we will need to sample at a higher rate to avoid losing information that is in the signal. The Sampling Theorem states that a signal can be exactly reproduced if it is sampled at a frequency F, where F is greater than twice the maximum frequency in the signal.

What is sampling theorem in communication system?

The sampling theorem states that, “a signal can be exactly reproduced if it is sampled at the rate fs which is greater than twice the maximum frequency W.” To understand this sampling theorem, let us consider a band-limited signal, i.e., a signal whose value is non-zero between some –W and W Hertz.

Which theorem is used for communication system?

telecommunications systems commonly referred to as the sampling theorem, and the sampling interval (1/2B seconds) is referred to as the Nyquist interval (after the Swedish-born American electrical engineer Harry Nyquist).

What is information capacity theorem?

The theorem implies that error-free transmission is possible if we do not send information at a rate greater than the channel capacity. Thus, the information capacity theorem defines the fundamental limit on the rate of error-free transmission for a power limited, bandlimited Gaussian channel.

What is information capacity?

In electronic communication channels the information capacity is the maximum amount of information that can pass through a channel without error, i.e., it is a measure of channel “goodness.” The actual amount of information depends on the code— how information is represented.

What does the sampling theorem say about a signal?

The sampling theorem states that, “a signal can be exactly reproduced if it is sampled at the rate f s which is greater than twice the maximum frequency W.”. To understand this sampling theorem, let us consider a band-limited signal, i.e., a signal whose value is non-zero between some –W and W Hertz.

How is the sampling theorem used in reconstruction?

And these types of discrete signals are well performed in the reconstruction process for recovering the original signal. The sampling theorem can be defined as the conversion of an analog signal into a discrete form by taking the sampling frequency as twice the input analog signal frequency.

How are sample points used in Shannon sampling theorem?

After the analog signal is sampled, we obtain the sampled signal whose amplitude values are taken at the sampling instants, thus the processor is able to handle the sample points. Next, we have to ensure that samples are collected at a rate high enough that the original analog signal can be reconstructed or recovered later.

What is the definition of the Nyquist sampling theorem?

Nyquist sampling theorem states that the sampling signal frequency should be double the input signal’s highest frequency component to get distortion less output signal. As per the scientist’s name, Harry Nyquist this is named as Nyquist sampling theorem. Fs=2Fm.