# Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors

arXiv: https://arxiv.org/abs/1707.08908 · HTML: https://mlsanity.com/paper/1707.08908/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2017.

## Built on by (most cited first, 30 of 44)

- [An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation](https://mlsanity.com/paper/2110.04980/) (2021)
- [Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification](https://mlsanity.com/paper/2203.15932/) (2022)
- [Adaptive Visibility Graph Neural Network and its Application in Modulation Classification](https://mlsanity.com/paper/2106.08564/) (2021)
- [AMC-Net: An Effective Network for Automatic Modulation Classification](https://mlsanity.com/paper/2304.00445/) (2023)
- [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)
- [Deep Learning Radio Frequency Signal Classification with Hybrid Images](https://mlsanity.com/paper/2105.09063/) (2021)
- [Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers](https://mlsanity.com/paper/2403.15417/) (2024)
- [Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classification](https://mlsanity.com/paper/2408.05151/) (2024)
- [Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge](https://mlsanity.com/paper/2110.14357/) (2021)
- [MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts](https://mlsanity.com/paper/2312.02298/) (2023)
- [On the benefits of robust models in modulation recognition](https://mlsanity.com/paper/2103.14977/) (2021)
- [SafeAMC: Adversarial training for robust modulation recognition models](https://mlsanity.com/paper/2105.13746/) (2021)
- [AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities](https://mlsanity.com/paper/2502.05315/) (2025)
- [Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions](https://mlsanity.com/paper/2503.08091/) (2025)
- [Adapt under Attack and Domain Shift: Unified Adversarial Meta-Learning and Domain Adaptation for Robust Automatic Modulation Classification](https://mlsanity.com/paper/2511.01172/) (2025)
- [Digital Signal Processing Using Deep Neural Networks](https://mlsanity.com/paper/2109.10404/) (2021)
- [Self-supervised Learning for Clustering of Wireless Spectrum Activity](https://mlsanity.com/paper/2210.02899/) (2022)
- [Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing](https://mlsanity.com/paper/2212.13598/) (2022)
- [Real-time Traffic Classification for 5G NSA Encrypted Data Flows With Physical Channel Records](https://mlsanity.com/paper/2307.07756/) (2023)
- [Deep Feature Learning for Wireless Spectrum Data](https://mlsanity.com/paper/2308.03530/) (2023)
- [Federated Learning for UAV-Based Spectrum Sensing: Enhancing Accuracy Through SNR-Weighted Model Aggregation](https://mlsanity.com/paper/2411.11159/) (2024)
- [A Lightweight Deep Learning Model for Automatic Modulation Classification using Dual Path Deep Residual Shrinkage Network](https://mlsanity.com/paper/2507.04586/) (2025)
- [An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition](https://mlsanity.com/paper/2608.00796/) (2026)
- [Signal Processing Based Deep Learning for Blind Symbol Decoding and Modulation Classification](https://mlsanity.com/paper/2106.10543/) (2021)
- [Design of an Novel Spectrum Sensing Scheme Based on Long Short-Term Memory and Experimental Validation](https://mlsanity.com/paper/2111.10769/) (2021)
- [Deep Learning for Spectral Filling in Radio Frequency Applications](https://mlsanity.com/paper/2204.01536/) (2022)
- [Maximum Likelihood Distillation for Robust Modulation Classification](https://mlsanity.com/paper/2211.00748/) (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)

## Abstract (arXiv)

This paper looks into the technology classification problem for a distributed wireless spectrum sensing network. First, a new data-driven model for Automatic Modulation Classification (AMC) based on long short term memory (LSTM) is proposed. The model learns from the time domain amplitude and phase information of the modulation schemes present in the training data without requiring expert features like higher order cyclic moments. Analyses show that the proposed model yields an average classification accuracy of close to 90% at varying SNR conditions ranging from 0dB to 20dB. Further, we explore the utility of this LSTM model for a variable symbol rate scenario. We show that a LSTM based model can learn good representations of variable length time domain sequences, which is useful in classifying modulation signals with different symbol rates. The achieved accuracy of 75% on an input sample length of 64 for which it was not trained, substantiates the representation power of the model. To reduce the data communication overhead from distributed sensors, the feasibility of classification using averaged magnitude spectrum data, or online classification on the low cost sensors is studied. Furthermore, quantized realizations of the proposed models are analyzed for deployment on sensors with low processing power.