Equalizing a digital signal, involving Matlab

In summary, the problem at hand involves finding the input signal that went into a black box, given the output signal and a test signal with a specific frequency. This is done through analyzing the signals in the frequency domain and using the equation Y(ω) = H(ω)X(ω), where H(ω) represents the frequency function for all frequencies. The constants in H(ω) are multiplied by an exponential powered to iβω, but it is unclear which frequency should be used for the exponential. It is possible that the sampling frequency for the signal is the correct frequency to use, but this cannot be confirmed without further information or analysis.
  • #1
Inertigratus
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Homework Statement


A signal goes through a black box and out comes another signal.
Now I have the signal that came out and need to find the signal that went into the black box.
This by analyzing a test signal and the output of that test signal.
The analysis is done in the frequency domain. The test signal is using one specific frequency.
The signals are sampled using a specific frequency (different from the test signal frequency).


Homework Equations


Y(ω) = H(ω)X(ω)


The Attempt at a Solution


I know how the output depends on the input, but decided not to write it here since it's not necessary for my questions.
So I can find H(ω) by doing the division Y(ω)/X(ω) using the test input and output.
However this only gives me H(ω0) where ω0 is corresponding to the specific frequency used only for the test signal.
Using additional information (that I haven't provided here) I can find constants that make up the frequency function H(ω) for all frequencies.
However, the constants are multiplied (to make H(ω)) by an exponential powered to iβω.
Now what I'm wondering is, what frequency should I use, what "ω" should the exponential be powered to?
The sampling frequency?
Maybe what I'm asking is, which frequency is the "real" output using? the one for which I'm trying to find the input.
To clarify, H(ω) looks something like this: A0 + A1e-2πfβ. Which is for all frequencies, but I'm working numerically in Matlab and am not sure which frequency "f" is.
Isn't it simply the sampling frequency for the signal?
It's probably difficult for you people to get into this problem without having worked with it, hope I made some sense atleast.
 
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  • #2
Perhaps someone could take a look in Matlab?
I could send the code/files, it's not that much.
My code is pretty much finished, it's just that there's some (probably minor) mistake that I can't figure out.
Would be greatly appreciated!
 

Related to Equalizing a digital signal, involving Matlab

1. What is equalization of a digital signal and why is it important?

Equalization is the process of correcting distortions or imbalances in a digital signal to improve its quality. It is important because it ensures that the transmitted data is received accurately and as intended, without any errors or loss of information.

2. How does equalization work in Matlab?

In Matlab, equalization is typically performed using digital signal processing techniques. This involves using algorithms and filters to compensate for any distortions or imbalances in the signal, thereby improving its quality.

3. What are the different types of equalization methods available in Matlab?

There are various equalization methods available in Matlab, such as linear equalization, decision feedback equalization, and maximum likelihood sequence estimation. The choice of method depends on the characteristics of the signal and the type of distortion present.

4. Can equalization be applied to any type of digital signal?

Yes, equalization can be applied to any type of digital signal, including audio, video, and data signals. The principles and techniques of equalization remain the same, but the specific methods and algorithms may vary depending on the type of signal.

5. Are there any limitations to equalization in Matlab?

While Matlab offers powerful equalization tools, there are some limitations to consider. These include the complexity and computational resources required for certain equalization methods, as well as the effectiveness of equalization in correcting severe distortions or noise in the signal.

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