# Getting hilbert transform of IQ signal

Im new to communications so this may be a stupid question, but I am somewhat confused. I am trying to train an automatic modulation classification (AMC) algorithm on IQ data from the RadioML dataset. The data comes in IQ format (eg [1024, 2] matrix).

I am reading papers now which use feature based methods and talk about getting the hilbert transform / DFT of the received signal and extracting features from there (for example, the std deviation of the instantaneous amplitude, see Table 1 here).

My question is what do I take the hilbert transform/DFT of? Would it be the IQ signal (I+jQ) or is there something more else involved (multiplying by sin/cos)?

• The Hilbert transformation leaves you with your signal, and a copy of your signal rotated 90 degrees. I and Q are the signal and the signal rotated by 90 degrees. In other words, if you have I and Q, then something has already done a Hilbert transform (or approximation) for you. – JRE Feb 21 at 23:35
• also, maybe start with the papers of the authors of the RadioML dataset (that being Tim O'Shea et al, not researchers that do a survey paper). They're linked on the same web page as the dataset, when you click on "Publications". – Marcus Müller Feb 21 at 23:45
• @JRE my goal is to go from the IQ signals back to the 'recieved signal' as discussed in those papers. So does that mean I need to do an 'inverse' hilbert transform? – DankMasterDan Feb 21 at 23:52
• @MarcusMüller I read the original papers and looked at the data generation source code. I guess my question is more how the RadioML data relates to the 'received signal' discussed in the feature-based modulation classification literature (see linked paper above eq.3) – DankMasterDan Feb 21 at 23:53
• it is the received signal, as the computer gets it from the receiver. – Marcus Müller Feb 22 at 0:08