Extracting the threshold value of some given raw numbers
<p>I am trying to determine the conditions of a wireless channel by analysis of captured I/Q samples. Indeed, I have a 50000 data samples and as it is shown in the attached figure, there are some sparks in the graphs when there is an activity (e.g. data transmission) over the channel. I am trying to count the number of sparks which are data values higher than a threshold. </p>

<p>I need to have an accurate estimation of the threshold and then I can find the channel load. the threshold value in the attached figure is around 0.0025 and it should be noted that it varies over time. So, each time that I took 50000 samples, I have to find the threshold value first using some sort of unsupervised learning.</p>

<p>I tried k-means (in python scikit-learn) to cluster the data and find the centroids of the estimated clusters, but it can't give me good estimation on the threshold value (especially when there is no activity over the channel and the channel is idle).</p>

<p>I would like to know is there anyone who has prior experience on similar topics?</p>

<p>Captured data</p>

<p><a href="https://i.stack.imgur.com/xXfbG.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/xXfbG.png" alt="enter image description here"></a></p>