Shannon sampling theorem and Nyquist

In summary: Nyquist and Shannon to the sampling theorem. Nyquist showed that a channel of bandwidth B can transmit at a baud rate of 2B, while Shannon proved that a signal with bandwidth B can be reconstructed from samples taken at a rate of 2B. The sampling theorem is also known as the "shannon-nyquist theorem" because of their related results. However, some may argue that Nyquist's contribution was not as significant since he did not explicitly consider the problem of sampling and reconstruction of continuous signals. Shannon, on the other hand, addressed this issue in his work. This is why the two theorems are considered duals. Shannon's work is also supported by other mathematicians such as Kotelnik
  • #1
leright
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I understand that it was Nyquist who proved that a channel of bandwidth B can transmit at a baud rate of 2B. Also, Shannon, about 10 years later showed that if a signal is sampled at a rate of at least 2B, where B is the bandwidth of the bandlimited signal, then the signal can be reconstructed from the samples. I read that Nyquist's theorem implies shannon's theorem, but I do not see how this is the case.

It is for this reason that the sampling theorem is sometimes called the 'shannon-nyquist theorem'. However, I cannot make the connection between the two theorems. The only connection I see is the '2B' part. For this reason, I don't really see why Nyquist deserves any credit for the sampling theorem...
 
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  • #2
Taken from Wikipedia:

"The sampling theorem was implied by the work of Harry Nyquist in 1928 ("Certain topics in telegraph transmission theory"), in which he showed that up to 2B independent pulse samples could be sent through a system of bandwidth B; but he did not explicitly consider the problem of sampling and reconstruction of continuous signals. About the same time, Karl Küpfmüller showed a similar result[1], and discussed the sinc-function impulse response of a band-limiting filter, via its integral, the step response Integralsinus; this bandlimiting and reconstruction filter that is so central to the sampling theorem is sometimes referred to as a Küpfmüller filter (but seldom so in English).

The sampling theorem, essentially a dual of Nyquist's result, was proved by Claude E. Shannon in 1949 ("Communication in the presence of noise"). V. A. Kotelnikov published similar results in 1933 ("On the transmission capacity of the 'ether' and of cables in electrical communications", translation from the Russian), as did the mathematician E. T. Whittaker in 1915 ("Expansions of the Interpolation-Theory", "Theorie der Kardinalfunktionen"), J. M. Whittaker in 1935 ("Interpolatory function theory"), and Gabor in 1946 ("Theory of communication")."


http://en.wikipedia.org/wiki/Nyquist-Shannon_sampling_theorem
 
  • #3
I saw that article. I understand they are duals, but WHY are they duals? I cannot make the connection.
 
  • #4
...in which he showed that up to 2B independent pulse samples could be sent through a system of bandwidth B; but he did not explicitly consider the problem of sampling and reconstruction of continuous signals.

It doesn't seem that there's much more to it than that. Nyquist introduced the concept of a sampling rate... or a sampled signal, while Shannon deduced this result to explain that you need to sample 2B to reconstruct a continuous signal out of samples. I don't think Nyquist considered the problems of aliasing, etc. Shannon must have.
 
  • #5
Sending a signal with baud rate 2B is the inverse operation of sampling a signal with sample frequency 2B.

- Warren
 

Related to Shannon sampling theorem and Nyquist

1. What is the Shannon sampling theorem?

The Shannon sampling theorem, also known as the Nyquist-Shannon theorem or the Nyquist sampling theorem, is a fundamental concept in signal processing. It states that in order to accurately reconstruct a continuous signal from its samples, the sampling rate must be at least twice the highest frequency component of the signal.

2. Why is the Shannon sampling theorem important?

The Shannon sampling theorem is important because it provides a mathematical basis for understanding and analyzing sampled signals. It ensures that the original signal can be accurately reconstructed from its samples, which is essential in many applications such as digital audio and image processing.

3. What is the Nyquist rate?

The Nyquist rate, also known as the Nyquist frequency, is the minimum sampling rate required to accurately reconstruct a continuous signal according to the Shannon sampling theorem. It is equal to twice the highest frequency component of the signal.

4. How is the Nyquist rate related to the bandwidth of a signal?

The Nyquist rate is directly related to the bandwidth of a signal. The bandwidth is the range of frequencies that the signal contains. According to the Shannon sampling theorem, the sampling rate must be at least twice the bandwidth of the signal to accurately reconstruct it.

5. What happens if the sampling rate is below the Nyquist rate?

If the sampling rate is below the Nyquist rate, aliasing can occur. This means that higher frequency components of the signal will be incorrectly represented in the reconstructed signal, resulting in distortion. In order to avoid aliasing, the sampling rate must always be at least equal to the Nyquist rate.

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