Model an FPGA FM Radio Demodulator in Python

In summary, the conversation is about the question of whether the effects of undersampling can be replicated in the discrete time domain via resampling. The speaker is trying to model an FPGA FM radio demodulator in Python and is confident in their understanding of undersampling but is unsure about resampling. They have tried resampling with a high sample rate FM signal at 1.2MHz, but have not been successful. The speaker is considering whether resampling and undersampling are fundamentally different and is seeking further information or clarification. They also mention that they are not an expert in digital signal processing.
  • #1
Jyan
36
2
I am trying to model an FPGA FM radio demodulator in Python. I will have a mixer that converts an FM channel to 36MHz IF, and I plan on undersampling this IF signal. If in the digital world (my Python model), I have a high sample rate FM signal at 1.2MHz, should I be able to resample to a lower sample rate (after using a BPF ofc) and obtain the same aliasing down to baseband?

My understanding is that this should be possible, it just doesn't seem to be working. I am confident I understand undersampling 100%, but am not so sure about resampling... When I resample the FM modulated signal I get complete garbage at the baseband. My high sample rate is 3MSPs, the FM signal is sitting with 200KHz bandwidth and a centre at 1.2MHz. I am resampling to 525KSPs, if this were undersampling then the FM signal would be sitting in the 5th nyquist zone. But does it work the same digitally?
 
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  • #2
Thanks for the post! Sorry you aren't generating responses at the moment. Do you have any further information, come to any new conclusions or is it possible to reword the post?
 
  • #3
I can add some more details. For now I have kind of sidestepped the problem.

The essential question is whether the effects of undersampling, (see: https://en.wikipedia.org/wiki/Undersampling) can be replicated in the discrete time domain via resampling (see: https://en.wikipedia.org/wiki/Decimation_(signal_processing) , https://en.wikipedia.org/wiki/Upsampling). Currently, I think it is actually not possible, resampling and undersampling are fundamentally different, but I'm not certain. I am by no means an expert in digital signal processing.
 

1. What is an FPGA FM radio demodulator?

An FPGA FM radio demodulator is a circuit that is used to extract the audio signal from a frequency-modulated (FM) radio signal. It is implemented on a field-programmable gate array (FPGA) chip, which allows for flexibility and reconfigurability.

2. Why would someone want to model an FPGA FM radio demodulator in Python?

Modeling an FPGA FM radio demodulator in Python allows for a more cost-effective and efficient way to test and develop the circuit. It also allows for easier modification and optimization of the design.

3. What are the key components of an FPGA FM radio demodulator?

The key components of an FPGA FM radio demodulator include an FM receiver, a phase locked loop (PLL), a low-pass filter, and a demodulator. The FM receiver is responsible for receiving the FM signal, while the PLL helps to synchronize the signal. The low-pass filter removes high-frequency noise, and the demodulator extracts the audio signal.

4. Are there any limitations to using a Python-based model for an FPGA FM radio demodulator?

One limitation is that the Python-based model may not be as accurate as the actual hardware implementation. This is because the model does not take into account real-world factors such as noise, interference, and component tolerances. Additionally, the model may not be able to replicate timing constraints and other hardware-specific features.

5. Can the Python-based model be used for other types of radio demodulators?

Yes, the Python-based model can be adapted for other types of radio demodulators, such as AM or digital radio demodulators. The underlying principles and components are similar, so the model can be modified to suit different types of radio signals.

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