# Deep Feature Learning for Wireless Spectrum Data

arXiv: https://arxiv.org/abs/2308.03530 · HTML: https://mlsanity.com/paper/2308.03530/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors](https://mlsanity.com/paper/1707.08908/) (2017)
- [Electrosense: Open and Big Spectrum Data](https://mlsanity.com/paper/1703.09989/) (2017)
- [Semi-Supervised Radio Signal Identification](https://mlsanity.com/paper/1611.00303/) (2016)
- [Radio Access Technology Characterisation Through Object Detection](https://mlsanity.com/paper/2007.13561/) (2020)

## Built on by (most cited first, 1 of 1)

- [Neural Network Characterization and Entropy Regulated Data Balancing through Principal Component Analysis](https://mlsanity.com/paper/2312.01392/) (2023)

## Abstract (arXiv)

In recent years, the traditional feature engineering process for training machine learning models is being automated by the feature extraction layers integrated in deep learning architectures. In wireless networks, many studies were conducted in automatic learning of feature representations for domain-related challenges. However, most of the existing works assume some supervision along the learning process by using labels to optimize the model. In this paper, we investigate an approach to learning feature representations for wireless transmission clustering in a completely unsupervised manner, i.e. requiring no labels in the process. We propose a model based on convolutional neural networks that automatically learns a reduced dimensionality representation of the input data with 99.3% less components compared to a baseline principal component analysis (PCA). We show that the automatic representation learning is able to extract fine-grained clusters containing the shapes of the wireless transmission bursts, while the baseline enables only general separability of the data based on the background noise.