# Deep Learning for Spectrum Sensing

arXiv: https://arxiv.org/abs/1909.02730 · HTML: https://mlsanity.com/paper/1909.02730/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2019.

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

- [SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensing](https://mlsanity.com/paper/2405.15542/) (2024)
- [Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning](https://mlsanity.com/paper/2208.03694/) (2022)
- [SpectrumFM: A Foundation Model for Intelligent Spectrum Management](https://mlsanity.com/paper/2505.06256/) (2025)
- [Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges](https://mlsanity.com/paper/2410.23949/) (2024)
- [Toward a Low-Cost Perception System in Autonomous Vehicles: A Spectrum Learning Approach](https://mlsanity.com/paper/2502.01940/) (2025)
- [Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals](https://mlsanity.com/paper/2209.13007/) (2022)
- [MASSFormer: Mobility-Aware Spectrum Sensing using Transformer-Driven Tiered Structure](https://mlsanity.com/paper/2409.17546/) (2024)
- [Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack](https://mlsanity.com/paper/2410.01393/) (2024)
- [Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks](https://mlsanity.com/paper/2410.12772/) (2024)
- [Joint Interference Detection and Identification via Adversarial Multi-task Learning](https://mlsanity.com/paper/2604.08607/) (2026)

## Abstract (arXiv)

In cognitive radio systems, the ability to accurately detect primary user's signal is essential to secondary user in order to utilize idle licensed spectrum. Conventional energy detector is a good choice for blind signal detection, while it suffers from the well-known SNR-wall due to noise uncertainty. In this letter, we firstly propose a deep learning based signal detector which exploits the underlying structural information of the modulated signals, and is shown to achieve the state of the art detection performance, requiring no prior knowledge about channel state information or background noise. In addition, the impacts of modulation scheme and sample length on performance are investigated. Finally, a deep learning based cooperative detection system is proposed, which is shown to provide substantial performance gain over conventional cooperative sensing methods.