In this blog, I will discuss from a communication system point of view about the Nyquist sample and CS theory. A communication system usually samples and processes a 1D signal. So here we are talk about 1D signal cases.

As shown in the figure, for a analog signal, we need to first sample it to digital signal, and then process it, and de-sample/reconstruct it into analogy signal again.

Therefore, the "process" part is called digital signal processing (DSP).

The Nyquist sampling happens in the "sampling" part, before DSP.

Then what does Nyquist say?

That is, given a signal with the highest frequency component being $f_m$. The sufficient condition for return from the digital signal $x[n]$ to $x(t)$ is that the sampling frequency $f_s$ is at least $2f_m$.

It should be noticed that this is only a sufficient, not necessary condition.

In certain cases, the condition is necessary, e.g., the signal is band-limited. If the whole limited band is occupied by the signal, or we don't know which portion of band the signal occupied, in other words, the only information we've got is the highest frequency of the signal,

(we call this kind of signal strict-sense band-limited signal), then the only way is to use Nyquist rate to sample the signal.

However, you may argue that, in practice, the signal may not band-limited, then what should we do?

The answer is making the signal band-limited before we sample it. This can be done using a LPF. And it is another story that how the band-limited signal approximates the real signal. Here we only focused on the already band-limited signal.

On the other hand, you may also ask that, if we have a band-pass signal, (of course, the band-pass signal is a band-limited signal, however, not strict sense band-limited signal), then can we have lower sampling frequency.

The answer is yes. To see the method, just think we move the band-pass signal to the base-band, and then it become a strict-sense band-limited signal. Then it is obvious how Nyquist rate works. Actually, we are given more information, i.e., the signal is band-passed from $f_L$ to $f_H$, thus we are able to sample the signal using frequency below $2f_H$.

In this case, we can call such sampling process under-sampling. However, it can be viewed as Nyquist sampling via some conversion.

Before we go into CS, I'd like to a more complete communication system.

As you see in the figure, all "information" after the dash line are in format of bits stream. And if we ignore all imperfection of the system in between, e.g. we have perfect channel, perfect modulation scheme. Then the process after dash line is distortion free.

Then we move before the dash line, as we shown in last section, the sampling and de-sampling process can be distortion free.

Then how about the quantization? It brings distortion, and the precision of the quantizer determines the intensity of the distortion.

However, you can set all these asides to understand CS.

As shown in the figure, A/D converter actually is responsible for sampling and quantizationg, D/A converter does reverse things.

And source encoder is responsible for compression.

Then consider, if we can sub-sample the signal, then we may not need to compress the signal. Then the only question is how to reconstruct the signal. CS theory just asserts the signal can be recovered faithfully, if the signal is sparse in certain domain, which is highly incoherent with time domain. For instance, the frequency domain is incoherent with time domain.

The CS system can be imaging as following,

$x[n]$ is corresponding to $r[n]$, however, in the real system, we actually do not have $x[n]$. Instead, we have already compressed $p[n]$, which corresponds to $s[n]$.

From $r[n]$ to $r(t)$ is not a problem. Then how to get $r[n]$ from $s[n]$? That's where CS works.

And as you can see, the A/D (D/A) converter becomes A/I (I/A) converter.

The sampling requirement reduced, while the reconstruction is more powerful. This is another kind of asymmetry compared to traditional "source encoding".

How can CS be more powerful?

Say, what if the signal is sparse in time domain, or in a domain coherent with time domain? Then, according to the CS theory, we should not under-sample in time domain as shown before. We need to under-sample in a domain incoherent with the sparsity domain.

Then how to design it? The most beautiful part of CS is RIP. The RIP says if you design the "compression" part as under-sampling signal in a domain, while the under-sampled part of the domain satisfies RIP, then the signal can be reconstructed faithfully.

To my understanding, (and certainly you can have your own interpretation), a matrix satisfies RIP essentially is a subset of domain, i.e., only a few of basis of the domain, while the domain is incoherent with almost every other domain. That's the "university" of CS theory.

An example is Gaussian matrix, which is almost incoherent with either time domain or frequency domain.

It is a little tricky, but if you take into account the role of "overwhelming probability", it would be easier to understand the inner connection.

I didn't go into the mathematical part of these relationships. But if you look at them as a black box, such interpretation does help.

Then the problem is how to first transform the signal into $\Phi$ domain, to make the following under-sampling possible.

This is the story of A/I converter. I won't go into this topic. But intuitively speaking, it takes the help of convolution/integrator.

And again, for 2D signals, like imaging system, even high-dimensional signals, the Nyquist sample and CS can be interpreted in another way. And a most vivid example is the "single-pixel camera".

I will discuss the 2D signal sampling, especially the imaging systems, in future.

### What does Nyquist say for 1D signal?

As shown in the figure, for a analog signal, we need to first sample it to digital signal, and then process it, and de-sample/reconstruct it into analogy signal again.

Therefore, the "process" part is called digital signal processing (DSP).

The Nyquist sampling happens in the "sampling" part, before DSP.

Then what does Nyquist say?

That is, given a signal with the highest frequency component being $f_m$. The sufficient condition for return from the digital signal $x[n]$ to $x(t)$ is that the sampling frequency $f_s$ is at least $2f_m$.

It should be noticed that this is only a sufficient, not necessary condition.

In certain cases, the condition is necessary, e.g., the signal is band-limited. If the whole limited band is occupied by the signal, or we don't know which portion of band the signal occupied, in other words, the only information we've got is the highest frequency of the signal,

(we call this kind of signal strict-sense band-limited signal), then the only way is to use Nyquist rate to sample the signal.

However, you may argue that, in practice, the signal may not band-limited, then what should we do?

The answer is making the signal band-limited before we sample it. This can be done using a LPF. And it is another story that how the band-limited signal approximates the real signal. Here we only focused on the already band-limited signal.

On the other hand, you may also ask that, if we have a band-pass signal, (of course, the band-pass signal is a band-limited signal, however, not strict sense band-limited signal), then can we have lower sampling frequency.

The answer is yes. To see the method, just think we move the band-pass signal to the base-band, and then it become a strict-sense band-limited signal. Then it is obvious how Nyquist rate works. Actually, we are given more information, i.e., the signal is band-passed from $f_L$ to $f_H$, thus we are able to sample the signal using frequency below $2f_H$.

In this case, we can call such sampling process under-sampling. However, it can be viewed as Nyquist sampling via some conversion.

- It should also be noticed that here the sampling is uniform sampling. If you are interested in this topic, you can review some references in non-uniform sampling.
- Also, when it comes to 2D signal, it is a little tricky to see the Nyquist sampling. It's beyond this blog.

### What does CS say?

Before we go into CS, I'd like to a more complete communication system.

As you see in the figure, all "information" after the dash line are in format of bits stream. And if we ignore all imperfection of the system in between, e.g. we have perfect channel, perfect modulation scheme. Then the process after dash line is distortion free.

Then we move before the dash line, as we shown in last section, the sampling and de-sampling process can be distortion free.

Then how about the quantization? It brings distortion, and the precision of the quantizer determines the intensity of the distortion.

However, you can set all these asides to understand CS.

As shown in the figure, A/D converter actually is responsible for sampling and quantizationg, D/A converter does reverse things.

And source encoder is responsible for compression.

Then consider, if we can sub-sample the signal, then we may not need to compress the signal. Then the only question is how to reconstruct the signal. CS theory just asserts the signal can be recovered faithfully, if the signal is sparse in certain domain, which is highly incoherent with time domain. For instance, the frequency domain is incoherent with time domain.

The CS system can be imaging as following,

$x[n]$ is corresponding to $r[n]$, however, in the real system, we actually do not have $x[n]$. Instead, we have already compressed $p[n]$, which corresponds to $s[n]$.

From $r[n]$ to $r(t)$ is not a problem. Then how to get $r[n]$ from $s[n]$? That's where CS works.

And as you can see, the A/D (D/A) converter becomes A/I (I/A) converter.

The sampling requirement reduced, while the reconstruction is more powerful. This is another kind of asymmetry compared to traditional "source encoding".

How can CS be more powerful?

Say, what if the signal is sparse in time domain, or in a domain coherent with time domain? Then, according to the CS theory, we should not under-sample in time domain as shown before. We need to under-sample in a domain incoherent with the sparsity domain.

Then how to design it? The most beautiful part of CS is RIP. The RIP says if you design the "compression" part as under-sampling signal in a domain, while the under-sampled part of the domain satisfies RIP, then the signal can be reconstructed faithfully.

To my understanding, (and certainly you can have your own interpretation), a matrix satisfies RIP essentially is a subset of domain, i.e., only a few of basis of the domain, while the domain is incoherent with almost every other domain. That's the "university" of CS theory.

An example is Gaussian matrix, which is almost incoherent with either time domain or frequency domain.

It is a little tricky, but if you take into account the role of "overwhelming probability", it would be easier to understand the inner connection.

I didn't go into the mathematical part of these relationships. But if you look at them as a black box, such interpretation does help.

Then the problem is how to first transform the signal into $\Phi$ domain, to make the following under-sampling possible.

This is the story of A/I converter. I won't go into this topic. But intuitively speaking, it takes the help of convolution/integrator.

And again, for 2D signals, like imaging system, even high-dimensional signals, the Nyquist sample and CS can be interpreted in another way. And a most vivid example is the "single-pixel camera".

I will discuss the 2D signal sampling, especially the imaging systems, in future.

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