# Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression

arXiv: https://arxiv.org/abs/2203.00517 · HTML: https://mlsanity.com/paper/2203.00517/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 71 references that are in the map.
- First posted on arXiv 26 Feb 2022.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Which Tasks Should Be Learned Together in Multi-task Learning?](https://mlsanity.com/paper/1905.07553/) (2019)
- [Deep Audio-Visual Speech Recognition](https://mlsanity.com/paper/1809.02108/) (2018)
- [Quantized Convolutional Neural Networks for Mobile Devices](https://mlsanity.com/paper/1512.06473/) (2015)
- [Fixed Point Quantization of Deep Convolutional Networks](https://mlsanity.com/paper/1511.06393/) (2015)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/1901.07947/) (2019)
- [A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization](https://mlsanity.com/paper/1812.06081/) (2018)
- [Sentiment and Sarcasm Classification with Multitask Learning](https://mlsanity.com/paper/1901.08014/) (2019)
- [Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification](https://mlsanity.com/paper/2101.10254/) (2021)
- [Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning with Deep Unfolding](https://mlsanity.com/paper/2004.10715/) (2020)
- [A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges](https://mlsanity.com/paper/2201.00680/) (2022)
- [Wireless Interference Identification with Convolutional Neural Networks](https://mlsanity.com/paper/1703.00737/) (2017)

## Built on by (most cited first, 10 of 10)

- [Multi-task Learning for Radar Signal Characterisation](https://mlsanity.com/paper/2306.13105/) (2023)
- [IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G](https://mlsanity.com/paper/2506.06718/) (2025)
- [RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth](https://mlsanity.com/paper/2209.03142/) (2022)
- [Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth](https://mlsanity.com/paper/2210.02897/) (2022)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [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)
- [Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices](https://mlsanity.com/paper/2303.13538/) (2023)
- [ELC: Evidential Lifelong Classifier for Uncertainty Aware Radar Pulse Classification](https://mlsanity.com/paper/2604.06958/) (2026)
- [Equitable Multi-Task Learning for AI-RANs](https://mlsanity.com/paper/2603.08717/) (2026)
- [Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification](https://mlsanity.com/paper/2610.18404/) (2026)

## Abstract (arXiv)

Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness on the edge has the potential to serve as a key enabler for the emerging beyond 5G networks. State-of-the-art studies in this domain have (i) only focused on a single task - modulation or signal (protocol) classification - which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks based multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks while considering heterogeneous wireless signals such as radar and communication waveforms in the electromagnetic spectrum. 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. We additionally include experimental evaluations of the model with over-the-air collected samples and demonstrate first-hand insight on model compression along with deep learning pipeline for deployment on resource-constrained edge devices. We demonstrate significant computational, memory, and accuracy improvement of the proposed model over two reference architectures. In addition to modeling a lightweight MTL model suitable for resource-constrained embedded radio platforms, we provide a comprehensive heterogeneous wireless signals dataset for public use.