Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks

TL;DR

This paper explores using a neural network for non-linear self-interference cancellation in full-duplex radios, showing it matches traditional methods with less computational effort.

Contribution

It introduces a neural network-based non-linear cancellation method that outperforms polynomial-based algorithms in complexity while maintaining high cancellation performance.

Findings

Neural network canceler matches polynomial-based performance.

Neural network achieves lower computational complexity.

Measurement results validate the effectiveness of the neural approach.

Abstract

Full-duplex systems require very strong self-interference cancellation in order to operate correctly and a significant part of the self-interference signal is due to non-linear effects created by various transceiver impairments. As such, linear cancellation alone is usually not sufficient and sophisticated non-linear cancellation algorithms have been proposed in the literature. In this work, we investigate the use of a neural network as an alternative to the traditional non-linear cancellation method that is based on polynomial basis functions. Measurement results from a full-duplex testbed demonstrate that a small and simple feed-forward neural network canceler works exceptionally well, as it can match the performance of the polynomial non-linear canceler with significantly lower computational complexity.