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

arXiv: https://arxiv.org/abs/1812.00449 · HTML: https://mlsanity.com/paper/1812.00449/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2018.

## Built on by (most cited first, 2 of 2)

- [Interference Suppression Using Deep Learning: Current Approaches and Open Challenges](https://mlsanity.com/paper/2112.08988/) (2021)
- [Hybrid-Layers Neural Network Architectures for Modeling the Self-Interference in Full-Duplex Systems](https://mlsanity.com/paper/2110.09997/) (2021)

## Abstract (arXiv)

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.