Although spectrum sensing is commonly used in modern wireless communications to determine spectrum resources, the rapid development of wireless communications has generated massive heterogeneous spectrum data, which has dramatically increased the complexity of spectrum sensing. Machine-learning-assisted spectrum sensing, as an emerging and promising technique, provides an effective way to find available spectrum resources through the analysis of big spectrum data. In this article, a bigdata- based intelligent spectrum sensing method is proposed to improve heterogeneous spectrum sensing. Specifically, a cooperative spectrum sensing network is designed and established to realize wide-area broadband spectrum sensing and obtain big spectrum data. The effectiveness of such a network has been verified through detection probability simulation. To improve the reliability of spectrum sensing data, the correlations of the big spectrum data in time domain, frequency domain and space domain have been investigated, and the spectrum similarity has been obtained. Then a novel dual-end machine learning model is proposed to improve the precision and real-time prediction of heterogeneous spectrum states. Furthermore, a big spectrum data clustering mechanism is adopted to facilitate data matching and heterogeneous spectrum prediction. Finally, the comprehensive spectrum state is obtained through heterogeneous spectrum data fusion.

中文翻译：

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基于大数据的5G异构频谱通信智能频谱感知

尽管频谱感测通常用于现代无线通信中以确定频谱资源，但是无线通信的迅速发展产生了大量的异构频谱数据，极大地增加了频谱感测的复杂性。机器学习辅助频谱感测作为一种新兴的有前途的技术，提供了一种通过分析大频谱数据来找到可用频谱资源的有效方法。本文提出了一种基于大数据的智能频谱感知方法，以改善异构频谱感知。具体地，设计并建立了协作频谱感知网络，以实现广域宽带频谱感知并获取大频谱数据。这种网络的有效性已经通过检测概率模拟得到了验证。为了提高频谱感知数据的可靠性，研究了大频谱数据在时域，频域和空域的相关性，并获得了频谱相似度。然后提出了一种新颖的双端机器学习模型，以提高异构频谱状态的精度和实时预测。此外，采用大光谱数据聚类机制来促进数据匹配和异构光谱预测。最后，通过异构光谱数据融合获得综合光谱状态。然后提出了一种新颖的双端机器学习模型，以提高异构频谱状态的精度和实时预测。此外，采用大光谱数据聚类机制来促进数据匹配和异构光谱预测。最后，通过异构光谱数据融合获得综合光谱状态。然后提出了一种新颖的双端机器学习模型，以提高异构频谱状态的精度和实时预测。此外，采用大光谱数据聚类机制来促进数据匹配和异构光谱预测。最后，通过异构光谱数据融合获得综合光谱状态。