# Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments

arXiv: https://arxiv.org/abs/1909.11800 · HTML: https://mlsanity.com/paper/1909.11800/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 28 of 28)

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- [Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning](https://mlsanity.com/paper/2207.03046/) (2022)
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- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks](https://mlsanity.com/paper/2204.03027/) (2022)
- [Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks](https://mlsanity.com/paper/2212.11194/) (2022)
- [Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning](https://mlsanity.com/paper/2107.12173/) (2021)
- [Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks](https://mlsanity.com/paper/2301.05250/) (2023)
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- [Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks](https://mlsanity.com/paper/2101.01847/) (2021)
- [Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation](https://mlsanity.com/paper/2410.10521/) (2024)
- [Zero-bias Deep Neural Network for Quickest RF Signal Surveillance](https://mlsanity.com/paper/2110.05797/) (2021)
- [DeepAoANet: Learning Angle of Arrival from Software Defined Radios with Deep Neural Networks](https://mlsanity.com/paper/2112.00695/) (2021)
- [Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT](https://mlsanity.com/paper/2105.15098/) (2021)
- [Intermittent Jamming against Telemetry and Telecommand of Satellite Systems and A Learning-driven Detection Strategy](https://mlsanity.com/paper/2107.06181/) (2021)
- [RF Signal Transformation and Classification using Deep Neural Networks](https://mlsanity.com/paper/2204.03564/) (2022)
- [Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning](https://mlsanity.com/paper/2212.11778/) (2022)
- [Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing](https://mlsanity.com/paper/2212.13598/) (2022)
- [Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning](https://mlsanity.com/paper/2312.16715/) (2023)
- [Augmenting Training Data with Vector-Quantized Variational Autoencoder for Classifying RF Signals](https://mlsanity.com/paper/2410.18283/) (2024)
- [Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning](https://mlsanity.com/paper/2412.21164/) (2024)
- [Open-set Classification of Common Waveforms Using A Deep Feed-forward Network and Binary Isolation Forest Models](https://mlsanity.com/paper/2110.00252/) (2021)
- [Automatic Machine Learning for Multi-Receiver CNN Technology Classifiers](https://mlsanity.com/paper/2204.13819/) (2022)
- [Dynamic Online Modulation Recognition using Incremental Learning](https://mlsanity.com/paper/2312.04718/) (2023)
- [T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge](https://mlsanity.com/paper/2401.04837/) (2024)
- [Compact convolutional neural networks for AI-based drone detection system](https://mlsanity.com/paper/2607.16455/) (2026)

## Abstract (arXiv)

Dynamic spectrum access (DSA) benefits from detection and classification of interference sources including in-network users, out-network users, and jammers that may all coexist in a wireless network. We present a deep learning based signal (modulation) classification solution in a realistic wireless network setting, where 1) signal types may change over time; 2) some signal types may be unknown for which there is no training data; 3) signals may be spoofed such as the smart jammers replaying other signal types; and 4) different signal types may be superimposed due to the interference from concurrent transmissions. For case 1, we apply continual learning and train a Convolutional Neural Network (CNN) using an Elastic Weight Consolidation (EWC) based loss. For case 2, we detect unknown signals via outlier detection applied to the outputs of convolutional layers using Minimum Covariance Determinant (MCD) and k-means clustering methods. For case 3, we extend the CNN structure to capture phase shifts due to radio hardware effects to identify the spoofing signal sources. For case 4, we apply blind source separation using Independent Component Analysis (ICA) to separate interfering signals. We utilize the signal classification results in a distributed scheduling protocol, where in-network (secondary) users employ signal classification scores to make channel access decisions and share the spectrum with each other while avoiding interference with out-network (primary) users and jammers. Compared with benchmark TDMA-based schemes, we show that distributed scheduling constructed upon signal classification results provides major improvements to in-network user throughput and out-network user success ratio.