Analysis of spectrum sensing using deep learning algorithms: CNNs and RNNs-Reference-Cited by-同舟云学术

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Analysis of spectrum sensing using deep learning algorithms: CNNs and RNNs

Published:2024-03 Issue:3 Volume:15 Page:102505
ISSN:2090-4479
Container-title:Ain Shams Engineering Journal
language:en
Short-container-title:Ain Shams Engineering Journal

Author:

Kumar Arun,Gaur Nishant^ORCID,Chakravarty Sumit,Alsharif Mohammed H.,Uthansakul Peerapong^ORCID,Uthansakul Monthippa

Publisher

Elsevier BV

Subject

General Engineering

Reference44 articles.

1. NOMA based CR for QAM-64 and QAM-256;Kumar;Egyp Informat J,2020

2. Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology;Cai;Intelligent Converged Networks,2020

3. Pietra-Ricci Index Detector for Centralized Data Fusion Cooperative Spectrum Sensing;Guimarães;IEEE Trans Veh Technol,2020

4. Analysis of Hybrid Spectrum Sensing for 5G and 6G Waveforms;Kumar;Electronics,2023

5. Multiband Spectrum Sensing in Cognitive Radio Networks With Secondary User Hardware Limitation: Random and Adaptive Spectrum Sensing Strategies;Xiong;IEEE Trans Wirel Commun,2018

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1. A Hybrid Deep Learning Based Approach for Spectrum Sensing in Cognitive Radio;Physical Communication;2024-09

2. An efficient hybrid spectrum sensing algorithm to enhance the performance of optical NOMA waveforms using 256-QAM;Journal of Optical Communications;2024-08-27

3. Enhanced Atrous Convolution-Gated Recurrent Unit for Spectrum Sensing in Cognitive Radio Network;SN Computer Science;2024-08-14

4. Deep Learning-Based Boolean, Time Series, Error Detection, and Predictive Analysis in Container Crane Operations;Algorithms;2024-08-01

5. A novel probabilistic approach for driving the sustainable energy circular economy: Innovating efficiency in renewable resource markets;Sustainable Cities and Society;2024-08

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