# Multi-Label Wireless Interference Identification with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1804.04395 · HTML: https://mlsanity.com/paper/1804.04395/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

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

- [Interference Suppression Using Deep Learning: Current Approaches and Open Challenges](https://mlsanity.com/paper/2112.08988/) (2021)
- [Deep Learning Radio Frequency Signal Classification with Hybrid Images](https://mlsanity.com/paper/2105.09063/) (2021)
- [Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks](https://mlsanity.com/paper/2303.03326/) (2023)

## Abstract (arXiv)

The steadily growing use of license-free frequency bands require reliable coexistence management and therefore proper wireless interference identification (WII). In this work, we propose a WII approach based upon a deep convolutional neural network (CNN) which classifies multiple IEEE 802.15.1, IEEE 802.11 b/g and IEEE 802.15.4 interfering signals in the presence of a utilized signal. The generated multi-label dataset contains frequency- and time-limited sensing snapshots with the bandwidth of 10 MHz and duration of 12.8 μs, respectively. Each snapshot combines one utilized signal with up to multiple interfering signals. The approach shows promising results for same-technology interference with a classification accuracy of approximately 100 % for IEEE 802.15.1 and IEEE 802.15.4 signals. For IEEE 802.11 b/g signals the accuracy increases for cross-technology interference with at least 90 %.