# Deep Neural Network Architectures for Modulation Classification

arXiv: https://arxiv.org/abs/1712.00443 · HTML: https://mlsanity.com/paper/1712.00443/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2017.

## Built on by (most cited first, 18 of 18)

- [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)
- [SpectrumFM: A Foundation Model for Intelligent Spectrum Management](https://mlsanity.com/paper/2505.06256/) (2025)
- [Generalization Bounds for Neural Belief Propagation Decoders](https://mlsanity.com/paper/2305.10540/) (2023)
- [Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers](https://mlsanity.com/paper/2403.15417/) (2024)
- [Deep-Learning-Based Device Fingerprinting for Increased LoRa-IoT Security: Sensitivity to Network Deployment Changes](https://mlsanity.com/paper/2208.14964/) (2022)
- [Joint Signal Detection and Automatic Modulation Classification via Deep Learning](https://mlsanity.com/paper/2405.00736/) (2024)
- [Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions](https://mlsanity.com/paper/2503.08091/) (2025)
- [Modulation Classification Through Deep Learning Using Resolution Transformed Spectrograms](https://mlsanity.com/paper/2306.04655/) (2023)
- [An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition](https://mlsanity.com/paper/2608.00796/) (2026)
- [Spectro-Temporal RF Identification using Deep Learning](https://mlsanity.com/paper/2107.05114/) (2021)
- [Edge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysis](https://mlsanity.com/paper/2404.15343/) (2024)
- [BiLSTM and Attention-Based Modulation Classification of Realistic Wireless Signals](https://mlsanity.com/paper/2408.07247/) (2024)
- [ModFus-DM: Explore the Representation in Modulated Signal Diffusion Generated Models](https://mlsanity.com/paper/2508.01719/) (2025)
- [Dualformer: Efficient Feature Extractor for Complex-valued Blind Communication Signal Analysis](https://mlsanity.com/paper/2606.31352/) (2026)
- [DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification](https://mlsanity.com/paper/2607.08031/) (2026)
- [Edge-Efficient Transformer for End-to-End RF Spectrum Monitoring](https://mlsanity.com/paper/2607.18285/) (2026)
- [HFEMCNet: A Compact Hybrid Frequency Enriched Multi Channel Network for Automatic Modulation Classification](https://mlsanity.com/paper/2609.22139/) (2026)

## Abstract (arXiv)

In this work, we investigate the value of employing deep learning for the task of wireless signal modulation recognition. Recently in [1], a framework has been introduced by generating a dataset using GNU radio that mimics the imperfections in a real wireless channel, and uses 10 different modulation types. Further, a convolutional neural network (CNN) architecture was developed and shown to deliver performance that exceeds that of expert-based approaches. Here, we follow the framework of [1] and find deep neural network architectures that deliver higher accuracy than the state of the art. We tested the architecture of [1] and found it to achieve an accuracy of approximately 75% of correctly recognizing the modulation type. We first tune the CNN architecture of [1] and find a design with four convolutional layers and two dense layers that gives an accuracy of approximately 83.8% at high SNR. We then develop architectures based on the recently introduced ideas of Residual Networks (ResNet [2]) and Densely Connected Networks (DenseNet [3]) to achieve high SNR accuracies of approximately 83.5% and 86.6%, respectively. Finally, we introduce a Convolutional Long Short-term Deep Neural Network (CLDNN [4]) to achieve an accuracy of approximately 88.5% at high SNR.