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

arXiv: https://arxiv.org/abs/1711.00379 · HTML: https://mlsanity.com/paper/1711.00379/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [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)
- [SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers](https://mlsanity.com/paper/2308.12591/) (2023)
- [Complex-valued Adaptive System Identification via Low-Rank Tensor Decomposition](https://mlsanity.com/paper/2306.16428/) (2023)

## Abstract (arXiv)

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.