Learning and deploying compression of radio signals

A method of training at least one machine-learning network to learn compact representations of radio frequency (RF) signals, the method performed by at least one processor executing instructions stored on at least one computer memory coupled to the at least one processor, the method comprising:

• determining a first RF signal to be compressed;
• using an encoder machine-learning network to process the first RF signal and generate a compressed signal;
• using a decoder machine-learning network to process the compressed signal and generate a second RF signal that represents a reconstruction of the first RF signal;
• calculating a measure of distance between the second RF signal and the first RF signal;
• calculating a measure of compression in the compressed signal;
• and
• updating at least one of the encoder machine-learning network or the decoder machine-learning network based on (i) the measure of distance between the second RF signal and the first RF signal, and (ii) the measure of compression in the compressed signal.