I am interested in monitoring the usage of the QO-100 WB transponder over several weeks or months, to obtain statistics about how full the transponder is, what bandwidths are used, which channels are occupied more often, etc., as well as statistics about the power of the signals and the DVB-S2 beacon. For this, we need to compute and record to disk waterfall data for later analysis. Maia SDR is ideal for this task, because it is easy to write a Python script that configures the spectrometer to a low rate, connects to the WebSocket to fetch spectrometer data, performs some integrations to lower the rate even more, and records data to disk.
For this project I’ve settled on using a sample rate of 20 Msps, which covers the whole transponder plus a few MHz of receiver noise floor on each side (this will be used to calibrate the receiver gain) and gives a frequency resolution of 4.9 kHz with Maia SDR’s 4096-point FFT. At this sample rate, I can set the Maia SDR spectrometer to 5 Hz and then perform 50 integrations in the Python script to obtain one spectrum average every 10 seconds.
Part of the interest of setting up this project is that the Python script can serve as an example of how to interface Maia SDR with other applications and scripts. In this post I will show how the system works and an initial evaluation of the data that I have recorder over a week. More detailed analysis of the data will come in future posts.