Department of Wireless Communications, Poznan University of Technology, 61-131 Poznan, Poland.
Sensors (Basel). 2019 Oct 8;19(19):4348. doi: 10.3390/s19194348.
The growing number of radio communication devices and limited spectrum resources are drivers for the development of new techniques of dynamic spectrum access and spectrum sharing. In order to make use of the spectrum opportunistically, the concept of cognitive radio was proposed, where intelligent decisions on transmission opportunities are based on spectrum sensing. In this paper, two Machine Learning (ML) algorithms, namely k-Nearest Neighbours and Random Forest, have been proposed to increase spectrum sensing performance. These algorithms have been applied to Energy Detection (ED) and Energy Vector-based data (EV) to detect the presence of a Fourth Generation (4G) Long-Term Evolution (LTE) signal for the purpose of utilizing the available resource blocks by a 5G new radio system. The algorithms capitalize on time, frequency and spatial dependencies in daily communication traffic. Research results show that the ML methods used can significantly improve the spectrum sensing performance if the input training data set is carefully chosen. The input data sets with ED decisions and energy values have been examined, and advantages and disadvantages of their real-life application have been analyzed.
日益增长的无线通信设备数量与有限的频谱资源，是推动动态频谱接入和频谱共享等新技术发展的主要因素。为了实现频谱的机会式利用，提出了认知无线电的概念，其中传输机会的智能决策是基于频谱感知的。在本文中，提出了两种机器学习（ML）算法，即 k-最近邻和随机森林，以提高频谱感知性能。这些算法已应用于能量检测（ED）和基于能量向量的数据（EV），用于检测第四代（4G）长期演进（LTE）信号的存在，以便 5G 新无线电系统利用可用的资源块。这些算法利用了日常通信流量中的时间、频率和空间相关性。研究结果表明，如果仔细选择输入训练数据集，那么所使用的 ML 方法可以显著提高频谱感知性能。对基于 ED 决策和能量值的输入数据集进行了检查，并分析了它们在实际应用中的优缺点。
