# Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers

arXiv: https://arxiv.org/abs/2403.15417 · HTML: https://mlsanity.com/paper/2403.15417/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors](https://mlsanity.com/paper/1707.08908/) (2017)
- [Fast Deep Learning for Automatic Modulation Classification](https://mlsanity.com/paper/1901.05850/) (2019)
- [Deep Neural Network Architectures for Modulation Classification](https://mlsanity.com/paper/1712.00443/) (2017)
- [Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channels](https://mlsanity.com/paper/1911.04970/) (2019)
- [Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility](https://mlsanity.com/paper/2303.15495/) (2023)
- [High-Capacity Complex Convolutional Neural Networks For I/Q Modulation Classification](https://mlsanity.com/paper/2010.10717/) (2020)
- [DeepFIR: Addressing the Wireless Channel Action in Physical-Layer Deep Learning](https://mlsanity.com/paper/2005.04226/) (2020)

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

- [INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection](https://mlsanity.com/paper/2411.10918/) (2024)
- [Real-Time Bus Departure Prediction Using Neural Networks for Smart IoT Public Bus Transit](https://mlsanity.com/paper/2501.10514/) (2025)
- [Decision Feedback In-Context Learning for Wireless Symbol Detection](https://mlsanity.com/paper/2503.16594/) (2025)
- [Dualformer: Efficient Feature Extractor for Complex-valued Blind Communication Signal Analysis](https://mlsanity.com/paper/2606.31352/) (2026)

## Abstract (arXiv)

Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems. This paper presents an innovative approach that leverages Transformer networks, initially designed for natural language processing, to address the challenges of efficient AMR. Our transformer network architecture is designed with the mindset of real-time edge computing on IoT devices. Four tokenization techniques are proposed and explored for creating proper embeddings of RF signals, specifically focusing on overcoming the limitations related to the model size often encountered in IoT scenarios. Extensive experiments reveal that our proposed method outperformed advanced deep learning techniques, achieving the highest recognition accuracy. Notably, our model achieves an accuracy of 65.75 on the RML2016 and 65.80 on the CSPB.ML.2018+ dataset.