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

• Published in: IEEE sensors journal Vol. 25; no. 13; pp. 23416 - 23426
• Main Authors: Zheng, Bingfeng, Wang, Yonghua, Xu, Guanghai, Li, Jiawen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification algorithms; Cognitive radio (CR); Cooperative communication; cooperative spectrum sensing (CSS); Deep learning; deep learning (DL); Empirical mode decomposition; ensemble empirical mode decomposition (EEMD); False alarms; Fuses; Interference; Machine learning; Neural networks; Radio spectrum management; Riemannian mean; Sensors; Signal to noise ratio
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3493612

Conventional spectrum sensing (SS) algorithms demonstrate subpar detection performance in complex environments. In this study, we use a strategy that integrates deep learning (DL) with signal-denoising techniques. To improve detection performance and reduce noise interference, the ensemble empirical mode decomposition (EEMD) algorithm is used to eliminate noise components from the signal. Subsequently, a Riemannian mean-based algorithm is used to fuse multiple sensing data in a multiantenna system. The fused data serve as inputs to a dual-channel composite neural network (DCCNN), leading to the development of an EEMD-DCCNN-based SS 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 <inline-formula> <tex-math notation="LaTeX">= -18 </tex-math></inline-formula> dB.