# Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

arXiv: https://arxiv.org/abs/2112.08988 · HTML: https://mlsanity.com/paper/2112.08988/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 187 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 30 of 36)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Privacy in Deep Learning: A Survey](https://mlsanity.com/paper/2004.12254/) (2020)
- [Model Complexity of Deep Learning: A Survey](https://mlsanity.com/paper/2103.05127/) (2021)
- [Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments](https://mlsanity.com/paper/1909.11800/) (2019)
- [An Iterative BP-CNN Architecture for Channel Decoding](https://mlsanity.com/paper/1707.05697/) (2017)
- [Wireless Interference Identification with Convolutional Neural Networks](https://mlsanity.com/paper/1703.00737/) (2017)
- [Nonlinear Interference Mitigation via Deep Neural Networks](https://mlsanity.com/paper/1710.06234/) (2017)
- [End-to-end Learning from Spectrum Data: A Deep Learning approach for Wireless Signal Identification in Spectrum Monitoring applications](https://mlsanity.com/paper/1712.03987/) (2017)
- [Physical Layer Communications System Design Over-the-Air Using Adversarial Networks](https://mlsanity.com/paper/1803.03145/) (2018)
- [Machine Learning Approach to RF Transmitter Identification](https://mlsanity.com/paper/1711.01559/) (2017)
- [Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation](https://mlsanity.com/paper/2007.11102/) (2020)
- [Transmitter Classification With Supervised Deep Learning](https://mlsanity.com/paper/1905.07923/) (2019)
- [Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation](https://mlsanity.com/paper/2105.00929/) (2021)
- [Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural Networks](https://mlsanity.com/paper/1906.10044/) (2019)
- [A Deep Learning Approach for Automotive Radar Interference Mitigation](https://mlsanity.com/paper/1903.06380/) (2019)
- [Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks](https://mlsanity.com/paper/1711.00379/) (2017)
- [CFAR-Based Interference Mitigation for FMCW Automotive Radar Systems](https://mlsanity.com/paper/2101.01257/) (2021)
- [Multi-Label Wireless Interference Identification with Convolutional Neural Networks](https://mlsanity.com/paper/1804.04395/) (2018)
- [Deep Sensing: Cooperative Spectrum Sensing Based on Convolutional Neural Networks](https://mlsanity.com/paper/1705.08164/) (2017)
- [DSIC: Deep Learning based Self-Interference Cancellation for In-Band Full Duplex Wireless](https://mlsanity.com/paper/1811.01498/) (2018)

## Built on by (most cited first, 10 of 10)

- [Score-based Source Separation with Applications to Digital Communication Signals](https://mlsanity.com/paper/2306.14411/) (2023)
- [RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge](https://mlsanity.com/paper/2409.08839/) (2024)
- [End-to-End Autoencoder Communications with Optimized Interference Suppression](https://mlsanity.com/paper/2201.01388/) (2021)
- [Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks](https://mlsanity.com/paper/2303.03326/) (2023)
- [Aircraft Radar Altimeter Interference Mitigation Through a CNN-Layer Only Denoising Autoencoder Architecture](https://mlsanity.com/paper/2410.03423/) (2024)
- [Cross-Technology Interference: Detection, Avoidance, and Coexistence Mechanisms in the ISM Bands](https://mlsanity.com/paper/2501.06446/) (2025)
- [Temporal Convolutional Autoencoder for Interference Mitigation in FMCW Radar Altimeters](https://mlsanity.com/paper/2505.22783/) (2025)
- [Brownian Bridge Diffusion-Based Joint Channel Estimation and Data Detection for Jamming-Resilient Receivers](https://mlsanity.com/paper/2606.28778/) (2026)
- [Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems](https://mlsanity.com/paper/2607.08717/) (2026)
- [Calibrated RF-Fingerprinting Under Interference With Heterogeneous Transmission Protocols](https://mlsanity.com/paper/2609.20765/) (2026)

## Abstract (arXiv)

In light of the finite nature of the wireless spectrum and the increasing demand for spectrum use arising from recent technological breakthroughs in wireless communication, the problem of interference continues to persist. Despite recent advancements in resolving interference issues, interference still presents a difficult challenge to effective usage of the spectrum. This is partly due to the rise in the use of license-free and managed shared bands for Wi-Fi, long term evolution (LTE) unlicensed (LTE-U), LTE licensed assisted access (LAA), 5G NR, and other opportunistic spectrum access solutions. As a result of this, the need for efficient spectrum usage schemes that are robust against interference has never been more important. In the past, most solutions to interference have addressed the problem by using avoidance techniques as well as non-AI mitigation approaches (for example, adaptive filters). The key downside to non-AI techniques is the need for domain expertise in the extraction or exploitation of signal features such as cyclostationarity, bandwidth and modulation of the interfering signals. More recently, researchers have successfully explored AI/ML enabled physical (PHY) layer techniques, especially deep learning which reduces or compensates for the interfering signal instead of simply avoiding it. The underlying idea of ML based approaches is to learn the interference or the interference characteristics from the data, thereby sidelining the need for domain expertise in suppressing the interference. In this paper, we review a wide range of techniques that have used deep learning to suppress interference. We provide comparison and guidelines for many different types of deep learning techniques in interference suppression. In addition, we highlight challenges and potential future research directions for the successful adoption of deep learning in interference suppression.