I will explain how to do the test.
The simplest way is the following:
a) if possible, use a well known, indipendent source (for example we’re using a USRP B210) to emit the test signal. If you can’t, try to use the LimeSDR( but it could be difficult to emit and read 2 channels at the same time).
Use a splitter and calbe of the same length to distribute the signal at the input of LimeSDR RX channels.
b) previously generate a IQ file with a chirp signal: a complex sine wave whose frequency increases at a linear rate with time (for example, a freq swap from -1MHz to +1MHz)
c) Prepare LimeSDR-based code or a model that:
- read two channels a window of data
- calculate the phase of both signals
- calculate phase difference
- visualize the result (for example as histogram)
d) start the reading without external signal (in order to allow a correct calibration)
e) start emitting
f) observe te result
After playing a bit with all the parameters (like the number of points, the emitted power, the RX gain, etc), when the test is correctly prapared, you should see a uniformly distributed histogram.
At each repetition of the test, you will observe two things:
a) the center of the uniformly distributed histogram changes (offset)
b) the limits (that is the width of the histogram) change
the b) parameter is proportional to the slope of the linear dependence. We found that the slope is not constant among test repetitions.
If you repeat the test, for example, by changing the frequency swap (for example from -2MHz to 2MHz) you will see that the histogram is wider.
You can also do another thing, that maybe it’s easier but gives you the same result.
a) emit a complex sine.
b) observe the mean value of the gaussian you obtain as result
c) emit a complex sine at difference frequency
d) observe the mean value of the gaussian
If you have an external source, you can change the frequency without stopping the reading process. This is important because each restart in the reading process sadly produces an alteration of the result.
You’ll observe that the “distance” of the mean values of the gaussinas change proportionally to the frequency distance of the test signals.
I don’t know if I was clear in my explaination.
Please, could you try to do this test, and share the result?