ArticleSensors (Basel, Switzerland)2024
Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Adaptive frequency-domain CFAR for robust spectrum sensing under jamming and administrator-controlled counter-access.Scientific reports · 2026Article
- Intelligent Spectrum Sensing for NOMA Systems: A Cost-Sensitive LightGBM Approach with Objective-Driven Learning.Sensors (Basel, Switzerland) · 2026Article
- Article
- Efficient Deep Wavelet Gaussian Markov Dempster-Shafer Network-Based Spectrum Sensing at Very Low SNR in Cognitive Radio Networks.Sensors (Basel, Switzerland) · 2025Article
- Optimized multi scale graph neural network with attention mechanism for cooperative spectrum sensing in cognitive radio networks.Scientific reports · 2025Article
- Gramian Angular Field and Convolutional Neural Networks for Real-Time Multiband Spectrum Sensing in Cognitive Radio Networks.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order for cognitive radios to identify and take advantage of unused frequency bands, spectrum sensing is essential. Conventional techniques for spectrum sensing rely on extracting features from received signals at specific locations. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have recently demonstrated promise in improving the precision and efficacy of spectrum sensing. Our research introduces a groundbreaking approach to spectrum sensing by leveraging convolutional neural networks (CNNs) to significantly advance the precision and effectiveness of identifying unused frequency bands. We treat spectrum sensing as a classification task and train our model with diverse signal types and noise data, enabling unparalleled adaptability to novel signals. Our method surpasses traditional techniques such as the maximum-minimum eigenvalue ratio-based and frequency domain entropy-based methods, showcasing superior performance and adaptability. In particular, our CNN-based approach demonstrates exceptional accuracy, even outperforming established methods when faced with additive white Gaussian noise (AWGN).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.