Design and Implementation of a Neural Network Aided Self-Interference Cancellation Scheme for Full-Duplex Radios

TL;DR

This paper presents a neural network-based self-interference cancellation scheme for full-duplex radios, demonstrating higher throughput and resource efficiency compared to traditional polynomial methods.

Contribution

It introduces a novel hardware architecture for neural network-based non-linear self-interference cancellation in full-duplex radios.

Findings

Neural network canceller achieves comparable cancellation performance to polynomial methods.

Neural network implementation requires fewer hardware resources.

Neural network canceller has significantly higher throughput.

Abstract

In-band full-duplex systems are able to transmit and receive information simultaneously on the same frequency band. Due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self-interference cancellation is essential. In this work, we present a hardware architecture for a neural network based non-linear self-interference canceller and we compare it with our own hardware implementation of a conventional polynomial based canceller. We show that, for the same cancellation performance, the neural network canceller has a significantly higher throughput and requires fewer hardware resources.