# Towards an AI-Driven Universal Anti-Jamming Solution with Convolutional Interference Cancellation Network

arXiv: https://arxiv.org/abs/2203.09717 · HTML: https://mlsanity.com/paper/2203.09717/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares](https://mlsanity.com/paper/1904.12838/) (2019)
- [Deep Learning for Launching and Mitigating Wireless Jamming Attacks](https://mlsanity.com/paper/1807.02567/) (2018)

## Built on by (most cited first, 3 of 3)

- [Attention-Based Fusion of IQ and FFT Spectrograms with AoA Features for GNSS Jammer Localization](https://mlsanity.com/paper/2507.14167/) (2025)
- [GNSS Jammer Direction Finding in Dynamic Scenarios Using an Inertial-based Multi-Antenna System](https://mlsanity.com/paper/2512.05128/) (2025)
- [Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios](https://mlsanity.com/paper/2607.02537/) (2026)

## Abstract (arXiv)

Wireless links are increasingly used to deliver critical services, while intentional interference (jamming) remains a very serious threat to such services. In this paper, we are concerned with the design and evaluation of a universal anti-jamming building block, that is agnostic to the specifics of the communication link and can therefore be combined with existing technologies. We believe that such a block should not require explicit probes, sounding, training sequences, channel estimation, or even the cooperation of the transmitter. To meet these requirements, we propose an approach that relies on advances in Machine Learning, and the promises of neural accelerators and software defined radios. We identify and address multiple challenges, resulting in a convolutional neural network architecture and models for a multi-antenna system to infer the existence of interference, the number of interfering emissions and their respective phases. This information is continuously fed into an algorithm that cancels the interfering signal. We develop a two-antenna prototype system and evaluate our jamming cancellation approach in various environment settings and modulation schemes using Software Defined Radio platforms. We demonstrate that the receiving node equipped with our approach can detect a jammer with over 99% of accuracy and achieve a Bit Error Rate (BER) as low as 10-6 even when the jammer power is nearly two orders of magnitude (18 dB) higher than the legitimate signal, and without requiring modifications to the link modulation. In non-adversarial settings, our approach can have other advantages such as detecting and mitigating collisions.