# Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification

arXiv: https://arxiv.org/abs/2101.10254 · HTML: https://mlsanity.com/paper/2101.10254/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Deep Audio-Visual Speech Recognition](https://mlsanity.com/paper/1809.02108/) (2018)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/1901.07947/) (2019)
- [Wireless Interference Identification with Convolutional Neural Networks](https://mlsanity.com/paper/1703.00737/) (2017)
- [Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment](https://mlsanity.com/paper/2009.03349/) (2020)

## Built on by (most cited first, 17 of 17)

- [A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges](https://mlsanity.com/paper/2201.00680/) (2022)
- [SciTS: Scientific Time Series Understanding and Generation with LLMs](https://mlsanity.com/paper/2510.03255/) (2025)
- [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)
- [FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning](https://mlsanity.com/paper/2203.13663/) (2022)
- [RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth](https://mlsanity.com/paper/2209.03142/) (2022)
- [Multi-stage Learning for Radar Pulse Activity Segmentation](https://mlsanity.com/paper/2312.09489/) (2023)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Multi-Task Learning as enabler for General-Purpose AI-native RAN](https://mlsanity.com/paper/2404.15197/) (2024)
- [Few-Shot Radar Signal Recognition through Self-Supervised Learning and Radio Frequency Domain Adaptation](https://mlsanity.com/paper/2501.03461/) (2025)
- [Deep neural network goes lighter: A case study of deep compression techniques on automatic RF modulation recognition for Beyond 5G networks](https://mlsanity.com/paper/2204.04390/) (2022)
- [Hierarchical Over-the-Air FedGradNorm](https://mlsanity.com/paper/2212.07414/) (2022)
- [Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning](https://mlsanity.com/paper/2312.16715/) (2023)
- [Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning](https://mlsanity.com/paper/2412.21164/) (2024)
- [The Turing Synthetic Radar Dataset: A dataset for pulse deinterleaving](https://mlsanity.com/paper/2602.03856/) (2026)
- [ATE-SG: Alternate Through the Epochs Stochastic Gradient for Multi-Task Neural Networks](https://mlsanity.com/paper/2312.16340/) (2023)
- [T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge](https://mlsanity.com/paper/2401.04837/) (2024)

## Abstract (arXiv)

Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, and secure communications. Consequently, it will become a key enabler with the emerging fifth-generation (5G) and beyond 5G communications, Internet of Things networks, among others. State-of-the-art studies in wireless signal recognition have only focused on a single task which in many cases is insufficient information for a system to act on. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks in conjunction with multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. Additionally, we consider the problem of heterogeneous wireless signals such as radar and communication signals in the electromagnetic spectrum. Accordingly, we have shown how the proposed MTL model outperforms several state-of-the-art single-task learning classifiers while maintaining a lighter architecture and performing two signal characterization tasks simultaneously. Finally, we also release the only known open heterogeneous wireless signals dataset that comprises of radar and communication signals with multiple labels.