Deep Denoising and Clustering-Based Cooperative Spectrum Sensing for Non-Orthogonal Multiple Access

被引:0
|
作者
Liao, Ningkang [1 ]
Zhang, Yongwei [2 ]
Wang, Yonghua [1 ]
Liu, Yang [3 ]
机构
[1] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China
[2] South China Agr Univ, Coll Math & Informat, Guangzhou 510642, Peoples R China
[3] Guangdong Univ Technol, Sch Informat Engn, Guangzhou 510006, Peoples R China
基金
中国国家自然科学基金;
关键词
Sensors; NOMA; Noise reduction; Feature extraction; Clustering algorithms; Signal to noise ratio; Training; Spectrum sensing; non-orthogonal multiple access; deep denoising; deep clustering; ALGORITHM;
D O I
10.1109/TCCN.2024.3427133
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Non-orthogonal multiple access (NOMA) technology offers higher communication throughput than its orthogonal multiple access counterpart. However, it also poses new challenges for spectrum sensing technology. Accurate spectrum sensing of a channel occupied by multiple users is challenging, especially in low signal-to-noise ratio environments. To improve the spectrum sensing performance, a spectrum sensing algorithm based on deep denoising and clustering is developed for power domain NOMA. First, a novel auto-encoder for deep denoising that can filter out the noise of signals is proposed. Then the auto-encoder is transplanted to a variational auto-encoder for extracting features with high separability. Finally, a ring K-means++ algorithm is proposed to classify features. In the experiments, simulations of algorithms are carried out in various scenarios with different numbers of primary users. The results show that the proposed algorithm outperforms other algorithms.
引用
收藏
页码:1831 / 1842
页数:12
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