# Wireless Interference Identification with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1703.00737 · HTML: https://mlsanity.com/paper/1703.00737/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2017.

## Built on by (most cited first, 13 of 13)

- [Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification](https://mlsanity.com/paper/2101.10254/) (2021)
- [A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges](https://mlsanity.com/paper/2201.00680/) (2022)
- [Interference Suppression Using Deep Learning: Current Approaches and Open Challenges](https://mlsanity.com/paper/2112.08988/) (2021)
- [Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression](https://mlsanity.com/paper/2203.00517/) (2022)
- [Deep Learning Radio Frequency Signal Classification with Hybrid Images](https://mlsanity.com/paper/2105.09063/) (2021)
- [RF Signal Classification with Synthetic Training Data and its Real-World Performance](https://mlsanity.com/paper/2206.12967/) (2022)
- [Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions](https://mlsanity.com/paper/2503.08091/) (2025)
- [Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks](https://mlsanity.com/paper/2303.03326/) (2023)
- [Spectro-Temporal RF Identification using Deep Learning](https://mlsanity.com/paper/2107.05114/) (2021)
- [Altitude Optimization of UAV Base Stations from Satellite Images Using Deep Neural Network](https://mlsanity.com/paper/2112.14551/) (2021)
- [T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge](https://mlsanity.com/paper/2401.04837/) (2024)
- [Joint Interference Detection and Identification via Adversarial Multi-task Learning](https://mlsanity.com/paper/2604.08607/) (2026)
- [Compound Interference Recognition for LR-FHSS Satellite IoT Uplinks via Multi-Domain Instance Fusion](https://mlsanity.com/paper/2607.11488/) (2026)

## Abstract (arXiv)

The steadily growing use of license-free frequency bands requires reliable coexistence management for deterministic medium utilization. For interference mitigation, proper wireless interference identification (WII) is essential. In this work we propose the first WII approach based upon deep convolutional neural networks (CNNs). The CNN naively learns its features through self-optimization during an extensive data-driven GPU-based training process. We propose a CNN example which is based upon sensing snapshots with a limited duration of 12.8 μs and an acquisition bandwidth of 10 MHz. The CNN differs between 15 classes. They represent packet transmissions of IEEE 802.11 b/g, IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the 2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII approaches and has a classification accuracy greater than 95% for signal-to-noise ratio of at least -5 dB.