  Machine learning (ML) has shown great promise in optimizing various aspects
of the physical layer processing in wireless communication systems. In this
paper, we use ML to learn jointly the transmit waveform and the
frequency-domain receiver. In particular, we consider a scenario where the
transmitter power amplifier is operating in a nonlinear manner, and ML is used
to optimize the waveform to minimize the out-of-band emissions. The system also
learns a constellation shape that facilitates pilotless detection by the
simultaneously learned receiver. The simulation results show that such an
end-to-end optimized system can communicate data more accurately and with less
out-of-band emissions than conventional systems, thereby demonstrating the
potential of ML in optimizing the air interface. To the best of our knowledge,
there are no prior works considering the power amplifier induced emissions in
an end-to-end learned system. These findings pave the way towards an ML-native
air interface, which could be one of the building blocks of 6G.
