I/Q of Signals and Hilbert Transform

In summary, to find the carrier frequency f0 in a signal, you would take the in-phase and quadrature-phase components and calculate the convolution.
  • #1
ashah99
60
2
Homework Statement
Please see below for problem statement.
Relevant Equations
In-phase component: [S(f) + S*(-f)]/2
Quadrature phase component: [S(f) - S*(-f)] / (2j)
Hilbert Transform: H(f) = -j*sign(f)
Hello, would anyone be willing to provide help to the following problem? I can find the Fourier Transform of the complex envelope of s(t) and the I/Q can be found by taking the Real and imaginary parts of that complex envelope, but how can I approach the actual question of finding the carrier f0? Do I multiply by -j*sign(f) of the Q part in the frequency domain and set it equal to I and solve for f0? I appreciate any help.

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  • #2
Hi, just thought I would give this a go because it is unanswered.

ashah99 said:
I can find the Fourier Transform of the complex envelope of s(t) and the I/Q can be found by taking the Real and imaginary parts of that complex envelope, but how can I approach the actual question of finding the carrier f0?
Apologies, would you mind explaining how you find the in-phase and quadrature-phase components of the signal. If I had to make an educated guess (let me know if I am wrong), I thought we would try to resolve onto the in-phase and quadrature signals. That is, if we defined cos as in-phase and sin as quadrature-phase something along the lines of:

[tex] \text{In-phase component } s_{I}(t) = \int s(t) cos(2\pi f_0 t) dt [/tex]
and
[tex] \text{Quadrature-phase component } s_{Q}(t) = \int s(t) sin(2\pi f_0 t) dt [/tex]

(or we could define them the other way around)

Before I type out the rest of the what I did does that seem reasonable (otherwise, I don't want to lead you astray)?

IF that seems fair, then those integrals look like Fourier transform integrals evaluated at ## f = 0 ##... and we know that the Fourier transform of a product in the time domain is the product of the Fourier transforms in the frequency domain...
- By duality we can find Fourier transform of s(t)
- We know the Fourier transforms of the ##sin(2 \pi f_0 t)## and ##cos(2\pi f_0 t)## for a general ## f_0 ##
- We could calculate the convolution for both in-phase and quadrature-components

If you agree with the above (feel free to correct and/or disagree with me), then we can apply the Hilbert transform visually to look where the two
 

FAQ: I/Q of Signals and Hilbert Transform

What is the I/Q of a signal?

The I/Q (In-phase and Quadrature) of a signal refers to the two components that make up a complex signal. The in-phase component represents the real part of the signal, while the quadrature component represents the imaginary part. Together, they form a complex signal that can be used to analyze and process a wide range of signals, such as in communication systems.

How is the I/Q of a signal measured?

The I/Q of a signal can be measured using a variety of techniques, such as a quadrature demodulator or a digital signal processing algorithm. These methods involve separating the in-phase and quadrature components of a signal and then measuring their amplitudes and phases.

What is the Hilbert Transform and how is it related to I/Q signals?

The Hilbert Transform is a mathematical operation that is used to create a complex signal from a real signal. It is closely related to I/Q signals because the quadrature component of an I/Q signal is essentially the result of applying the Hilbert Transform to the in-phase component.

What are the applications of I/Q signals and Hilbert Transform in signal processing?

I/Q signals and Hilbert Transform have a wide range of applications in signal processing, particularly in communication systems. They are used for tasks such as modulation and demodulation of signals, as well as for filtering and frequency conversion.

Can I/Q signals and Hilbert Transform be used in other fields besides signal processing?

Yes, I/Q signals and Hilbert Transform have applications in other fields such as image processing, radar systems, and medical imaging. In these fields, they are used for tasks such as image reconstruction and feature extraction.

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