  Research has shown that communications systems and receivers suffer from high
power adjacent channel signals, called blockers, that drive the radio frequency
(RF) front end into nonlinear operation. Since simple systems, such as the
Internet of Things (IoT), will coexist with sophisticated communications
transceivers, radars and other spectrum consumers, these need to be protected
employing a simple, yet adaptive solution to RF nonlinearity. This paper
therefore proposes a flexible data driven approach that uses a simple
artificial neural network (ANN) to aid in the removal of the third order
intermodulation distortion (IMD) as part of the demodulation process. We
introduce and numerically evaluate two artificial intelligence (AI)-enhanced
receivers-ANN as the IMD canceler and ANN as the demodulator. Our results show
that a simple ANN structure can significantly improve the bit error rate (BER)
performance of nonlinear receivers with strong blockers and that the ANN
architecture and configuration depends mainly on the RF front end
characteristics, such as the third order intercept point (IP3). We therefore
recommend that receivers have hardware tags and ways to monitor those over time
so that the AI and software radio processing stack can be effectively
customized and automatically updated to deal with changing operating
conditions.
