# Modulation classification via demodulation confidence?

I'm not sure if this is the right place to post this question, but will try anyway :-)

I recently started reading up on the field of automated modulation classification (AMC) where an algorithm tried to classify which modulation scheme an RF signal is using (eg BPSK, QPSK, etc..). Some if the common approaches include higher order statistics, deep neural networks, etc...

My question is, at least for digital modulation schemes with constellation diagrams, why cant this problem be solved by a very simple solution: try decoding the signal using various modulation schemes and see which gives you the most confidence. Confidence can be measured by something like mean distance to the closest constellation point of that scheme.

I realize that this may not be the fastest solution to the problem, and it cannot easily be applied to analog schemes (e.g. FM), but would it at least work? What would be its biggest issues?

Thanks

(PS: my background in not anywhere close to digital communications, so forgive any ignorance)

• One problem would be complexity: you'd have to implement and run all those decoders. But I think it's a nice idea, you should go ahead and simulate it and see well how it works. – MBaz Feb 13 '20 at 22:15

• Sadly, that's far from true. You forget that we're not only talking about BPSK, QPSK, $2^N$-QAM, but also of differential variants, a lot of legacy/low complexity modulations (ASKs, OOK), FSKs in the most wonderful pulse shapes, then spreading techniques… alone dicking around with multiple carriers makes that problem exponentially hard, not even incorporating the fact that as an observer, you rarely have perfect SNR, see the same Doppler or even have the same understanding of how long 1 s is. – Marcus Müller Feb 14 '20 at 0:19