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I want to estimate noise floor in the spectrum. I tested many methods using journals, but I couldn't find a stable method. Suggest that we have a spectrum consist of many signal and we want to estimate noise floor form signal.

enter image description here

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  • $\begingroup$ Is that over a set of real measurements? $\endgroup$ – A_A May 18 at 12:03
  • $\begingroup$ Every time I comment on one of your questions, it's deleted a couple hours later, and I still always comment the same: What's the context of this? Noise floor measurements depend on what kind of noise you're expecting, and what kind of signal you want to keep out of that measurement, and for what purpose that measurment is made. $\endgroup$ – Marcus Müller May 18 at 18:18
  • $\begingroup$ So, if you could, for every question you ask, always describe your motivation for asking it, that would be a really easy but effective improvement to our ability to answer them :) $\endgroup$ – Marcus Müller May 18 at 18:18
  • $\begingroup$ @Marcus Müller I think you haven't information about my questions, My questions are obvious and except of you no body has problem with them. Please don't worrisome. $\endgroup$ – Velma Benedict May 19 at 10:37
  • $\begingroup$ @A_A In a simulation environment. $\endgroup$ – Velma Benedict May 19 at 12:52
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You can get help best if you can share what kind of signal do you have. For example, in scenarios where you have a multipath channel time domain response or 'channel impulse response', you can use mdeian of median of windows as your noise floor.

Because 'channel impulse response' will have multiple narrow peaks in time domain corresponding to each path, therefore, using mean of the complete CIR would result in biasing due to the high amplitude peaks.

Median is a better choice to avoid biased results of noise floor but it will require sorting operation and based on the length of signal, that might be a problem and complexity increases as $O(N.logN)$.

So, in such scenario it is best to use average of median of windowed sub-sections of the signal. You divide the complete signal into windows of size 256 or 512 and compute median of these sub-sections which will be pretty fast and then take median/average of this vector of medians. That is a pretty good noise floor and works well in practical scenario as well.

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  • $\begingroup$ I added A image in my question. How I can calculate noise floor in this image? $\endgroup$ – Velma Benedict May 18 at 12:49
  • $\begingroup$ @VelmaBenedict the method I suggested will work fine with the kind of signal you have shown $\endgroup$ – DSP Rookie May 18 at 16:42

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