Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier

TL;DR

This paper demonstrates how machine learning can jointly optimize waveform design and receiver processing to reduce out-of-band emissions in nonlinear power amplifier scenarios, advancing towards ML-native wireless interfaces.

Contribution

It introduces an end-to-end learned system that accounts for power amplifier nonlinearity, reducing emissions and improving data accuracy, a novel approach in this context.

Findings

Reduced out-of-band emissions compared to conventional systems

Enhanced data communication accuracy

First to consider power amplifier nonlinearities in end-to-end learning

Abstract

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.