# An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification

arXiv: https://arxiv.org/abs/2202.09777 · HTML: https://mlsanity.com/paper/2202.09777/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 20 Feb 2022.

## Builds on (most cited first, 11 of 11)

- [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Deep Architectures for Modulation Recognition](https://mlsanity.com/paper/1703.09197/) (2017)
- [ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks](https://mlsanity.com/paper/1812.01124/) (2018)
- [DeepRadioID: Real-Time Channel-Resilient Optimization of Deep Learning-based Radio Fingerprinting Algorithms](https://mlsanity.com/paper/1904.07623/) (2019)
- [Deep Neural Network Feature Designs for RF Data-Driven Wireless Device Classification](https://mlsanity.com/paper/2105.02755/) (2021)
- [Robust Wireless Fingerprinting via Complex-Valued Neural Networks](https://mlsanity.com/paper/1905.09388/) (2019)
- [Deep Complex Networks for Protocol-Agnostic Radio Frequency Device Fingerprinting in the Wild](https://mlsanity.com/paper/1909.08703/) (2019)
- [Leveraging Multiple Transmissions and Receptions for Channel-Agnostic Deep Learning-Based Network Device Classification](https://mlsanity.com/paper/2109.03799/) (2021)

## Built on by (most cited first, 4 of 4)

- [Preserving Data Privacy for ML-driven Applications in Open Radio Access Networks](https://mlsanity.com/paper/2402.09710/) (2024)
- [The neural networks with tensor weights and emergent fermionic Wick rules in the large-width limit](https://mlsanity.com/paper/2507.05303/) (2025)
- [QUASAR: A Quantum-Classical Neural Network for SAR Satellite Physical-Layer Authentication](https://mlsanity.com/paper/2608.20240/) (2026)
- [CRFCAN: A Complex-Valued Cross-Domain Residual Network for Joint Channel and Phase Noise Estimation in Sub-THz OFDM Systems](https://mlsanity.com/paper/2609.12244/) (2026)

## Abstract (arXiv)

Recent deep neural network-based device classification studies show that complex-valued neural networks (CVNNs) yield higher classification accuracy than real-valued neural networks (RVNNs). Although this improvement is (intuitively) attributed to the complex nature of the input RF data (i.e., IQ symbols), no prior work has taken a closer look into analyzing such a trend in the context of wireless device identification. Our study provides a deeper understanding of this trend using real LoRa and WiFi RF datasets. We perform a deep dive into understanding the impact of (i) the input representation/type and (ii) the architectural layer of the neural network. For the input representation, we considered the IQ as well as the polar coordinates both partially and fully. For the architectural layer, we considered a series of ablation experiments that eliminate parts of the CVNN components. Our results show that CVNNs consistently outperform RVNNs counterpart in the various scenarios mentioned above, indicating that CVNNs are able to make better use of the joint information provided via the in-phase (I) and quadrature (Q) components of the signal.