# Over the Air Deep Learning Based Radio Signal Classification

arXiv: https://arxiv.org/abs/1712.04578 · HTML: https://mlsanity.com/paper/1712.04578/

## Key facts

- Cited by 121 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2017.

## Built on by (most cited first, 30 of 121)

- [SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models](https://mlsanity.com/paper/2410.14720/) (2024)
- [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2102.07572/) (2021)
- [A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models](https://mlsanity.com/paper/2406.07929/) (2024)
- [Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification](https://mlsanity.com/paper/2101.10254/) (2021)
- [Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction](https://mlsanity.com/paper/2210.03067/) (2022)
- [A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges](https://mlsanity.com/paper/2201.00680/) (2022)
- [An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation](https://mlsanity.com/paper/2110.04980/) (2021)
- [Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification](https://mlsanity.com/paper/2203.15932/) (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)
- [Multi-task Learning for Radar Signal Characterisation](https://mlsanity.com/paper/2306.13105/) (2023)
- [Adaptive Visibility Graph Neural Network and its Application in Modulation Classification](https://mlsanity.com/paper/2106.08564/) (2021)
- [Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications](https://mlsanity.com/paper/2207.00300/) (2022)
- [Large Scale Radio Frequency Signal Classification](https://mlsanity.com/paper/2207.09918/) (2022)
- [WAIR-D: Wireless AI Research Dataset](https://mlsanity.com/paper/2212.02159/) (2022)
- [A Novel Automatic Modulation Classification Scheme Based on Multi-Scale Networks](https://mlsanity.com/paper/2105.15037/) (2021)
- [Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction](https://mlsanity.com/paper/2401.01056/) (2024)
- [RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings](https://mlsanity.com/paper/2501.17888/) (2025)
- [SpectrumFM: A Foundation Model for Intelligent Spectrum Management](https://mlsanity.com/paper/2505.06256/) (2025)
- [Countermeasures Against Adversarial Examples in Radio Signal Classification](https://mlsanity.com/paper/2407.06796/) (2024)
- [Deep Learning Radio Frequency Signal Classification with Hybrid Images](https://mlsanity.com/paper/2105.09063/) (2021)
- [Multi-stage Learning for Radar Pulse Activity Segmentation](https://mlsanity.com/paper/2312.09489/) (2023)
- [Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers](https://mlsanity.com/paper/2403.15417/) (2024)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classification](https://mlsanity.com/paper/2408.05151/) (2024)
- [Modulation and Coding for NOMA and RSMA](https://mlsanity.com/paper/2410.00239/) (2024)
- [FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition](https://mlsanity.com/paper/2505.21571/) (2025)
- [Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification](https://mlsanity.com/paper/2103.03745/) (2021)
- [Gradient-based Adversarial Deep Modulation Classification with Data-driven Subsampling](https://mlsanity.com/paper/2104.06375/) (2021)
- [BCNN: Binary Complex Neural Network](https://mlsanity.com/paper/2104.10044/) (2021)

## Abstract (arXiv)

We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals. We consider a rigorous baseline method using higher order moments and strong boosted gradient tree classification and compare performance between the two approaches across a range of configurations and channel impairments. We consider the effects of carrier frequency offset, symbol rate, and multi-path fading in simulation and conduct over-the-air measurement of radio classification performance in the lab using software radios and compare performance and training strategies for both. Finally we conclude with a discussion of remaining problems, and design considerations for using such techniques.