Cooperative Spectrum Sensing with Ensemble Empirical Mode Decomposition and Dual-Channel Composite Neural Network for Complex Environments

Conventional spectrum sensing algorithms demonstrate subpar detection performance in complex environments. In this study, we employ a strategy that integrates deep learning with signal-denoising techniques. To improve detection performance and reduce noise interference, the ensemble empirical mode d...

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Bibliographic Details
Published in:IEEE sensors journal 2024-11, p.1-1
Main Authors: Zheng, Bingfeng, Wang, Yonghua, Xu, Guanghai, Li, Jiawen
Format: Article
Language:English
Subjects:
Cognitive radio
cooperative spectrum sensing
deep learning
ensemble empirical mode decomposition
Riemannian mean
Online Access:Get full text
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Summary:Conventional spectrum sensing algorithms demonstrate subpar detection performance in complex environments. In this study, we employ a strategy that integrates deep learning with signal-denoising techniques. To improve detection performance and reduce noise interference, the ensemble empirical mode decomposition (EEMD) algorithm is utilized to eliminate noise components from the signal. Subsequently, a Riemannian mean-based algorithm is employed to fuse multiple sensing data in a multi-antenna system. The fused data serve as inputs to a dual-channel composite neural network (DCCNN), leading to the development of an EEMD-DCCNN-based spectrum sensing algorithm. Experimental results demonstrate that the proposed algorithm significantly outperforms traditional algorithms regarding detection performance. In particular, the proposed algorithm achieves a detection probability of 94.6% and a false alarm probability of 3.5% at SNR = -18dB.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3493612