AI-Driven Demodulators for Nonlinear Receivers in Shared Spectrum with High-Power Blockers

TL;DR

This paper presents an AI-based approach using simple neural networks to mitigate RF nonlinearities caused by high-power blockers, improving demodulation accuracy in shared spectrum environments.

Contribution

It introduces a flexible, data-driven neural network method for canceling third order intermodulation distortion in nonlinear RF receivers, adaptable to varying hardware characteristics.

Findings

Neural networks significantly improve BER performance under nonlinear conditions.

The architecture depends on RF front end parameters like IP3.

Adaptive hardware monitoring can optimize AI-based RF nonlinearity mitigation.

Abstract

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.