Deep Learning Approaches for Spectrum Sensing in Cognitive Radio Networks-Reference-Cited by-同舟云学术

Deep Learning Approaches for Spectrum Sensing in Cognitive Radio Networks

Published:2022-10-30 Issue: Volume: Page:
ISSN:
Container-title:2022 25th International Symposium on Wireless Personal Multimedia Communications (WPMC)
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Author:

Syed Sadaf Nazneen¹^ORCID,Lazaridis Pavlos I.¹^ORCID,Khan Faheem A.¹^ORCID,Ahmed Qasim Zeeshan¹^ORCID,Hafeez Maryam¹^ORCID,Holmes Violeta¹^ORCID,Chochliouros Ioannis P.²,Zaharis Zaharias D.³

Affiliation:

1. School of Computing and Engineering, University of Huddersfield,Huddersfield,United Kingdom

2. Hellenic Telecommunications, Organization S.A. (OTE),Maroussi-Athens,Greece

3. School of Electrical and Computing Engineering, Aristotle University of Thessaloniki,Thessaloniki,Greece

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10014682/10014702/10014805.pdf?arnumber=10014805

Reference32 articles.

1. Reference Datasets for Training and Evaluating RF Signal Detection and Classification Models

2. Over-the-Air Deep Learning Based Radio Signal Classification

3. RF Datasets For Machine Learning;inc,2022

4. Review on Classical to Deep Spectrum Sensing in Cognitive Radio Networks

5. Spectrum sensing based on deep learning classification for cognitive radios

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1. A Survey on Cognitive Radio Network Spectrum Sensing using Deep Learning Methods;2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC);2024-06-05

2. Performance Analysis of a Deep Neural Network-Based Spectrum Sensing Approach;Mechanisms and Machine Science;2024

3. Performance analysis of cyclostationary spectrum sensing with dynamic thresholding using artificial neural network under varying signal to noise ratio and noise variance conditions;Journal of Intelligent & Fuzzy Systems;2023-08-01

4. Deep Neural Networks for Spectrum Sensing: A Review;IEEE Access;2023