# Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder

arXiv: https://arxiv.org/abs/2011.08295 · HTML: https://mlsanity.com/paper/2011.08295/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 19 of 19)

- [Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction](https://mlsanity.com/paper/2401.01056/) (2024)
- [RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings](https://mlsanity.com/paper/2501.17888/) (2025)
- [SpectrumFM: A Foundation Model for Intelligent Spectrum Management](https://mlsanity.com/paper/2505.06256/) (2025)
- [EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding](https://mlsanity.com/paper/2508.18785/) (2025)
- [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)
- [A Lightweight Deep Learning Model for Automatic Modulation Classification using Dual Path Deep Residual Shrinkage Network](https://mlsanity.com/paper/2507.04586/) (2025)
- [Reliable Inference in Edge-Cloud Model Cascades via Conformal Alignment](https://mlsanity.com/paper/2510.17543/) (2025)
- [Signal Processing Based Deep Learning for Blind Symbol Decoding and Modulation Classification](https://mlsanity.com/paper/2106.10543/) (2021)
- [ModFus-DM: Explore the Representation in Modulated Signal Diffusion Generated Models](https://mlsanity.com/paper/2508.01719/) (2025)
- [Federated Self-Supervised Modulation Classification under Non-IID and Imbalanced Data](https://mlsanity.com/paper/2510.04927/) (2025)
- [Cross-Validated Cross-Channel Self-Attention and Denoising for Automatic Modulation Classification](https://mlsanity.com/paper/2604.10054/) (2026)
- [ZoomSpec: A Physics-Guided Coarse-to-Fine Framework for Wideband Spectrum Sensing](https://mlsanity.com/paper/2604.13568/) (2026)
- [Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification](https://mlsanity.com/paper/2605.11875/) (2026)
- [End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing](https://mlsanity.com/paper/2606.24075/) (2026)
- [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)

Identification of the type of communication technology and/or modulation scheme based on detected radio signal are challenging problems encountered in a variety of applications including spectrum allocation and radio interference mitigation. They are rendered difficult due to a growing number of emitter types and varied effects of real-world channels upon the radio signal. Existing spectrum monitoring techniques are capable of acquiring massive amounts of radio and real-time spectrum data using compact sensors deployed in a variety of settings. However, state-of-the-art methods that use such data to classify emitter types and detect communication schemes struggle to achieve required levels of accuracy at a computational efficiency that would allow their implementation on low-cost computational platforms. In this paper, we present a learning framework based on an LSTM denoising auto-encoder designed to automatically extract stable and robust features from noisy radio signals, and infer modulation or technology type using the learned features. The algorithm utilizes a compact neural network architecture readily implemented on a low-cost computational platform while exceeding state-of-the-art accuracy. Results on realistic synthetic as well as over-the-air radio data demonstrate that the proposed framework reliably and efficiently classifies received radio signals, often demonstrating superior performance compared to state-of-the-art methods.