Computer Engineering and Networks Department, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
Institute of Industrial Technology, Korea University, Sejong 30019, Korea.
Sensors (Basel). 2021 Feb 24;21(5):1556. doi: 10.3390/s21051556.
Modulation detection techniques have received much attention in recent years due to their importance in the military and commercial applications, such as software-defined radio and cognitive radios. Most of the existing modulation detection algorithms address the detection dedicated to the non-cooperative systems only. In this work, we propose the detection of modulations in the multi-relay cooperative multiple-input multiple-output (MIMO) systems for 5G communications in the presence of spatially correlated channels and imperfect channel state information (CSI). At the destination node, we extract the higher-order statistics of the received signals as the discriminating features. After applying the principal component analysis technique, we carry out a comparative study between the random committee and the AdaBoost machine learning techniques (MLTs) at low signal-to-noise ratio. The efficiency metrics, including the true positive rate, false positive rate, precision, recall, F-Measure, and the time taken to build the model, are used for the performance comparison. The simulation results show that the use of the random committee MLT, compared to the AdaBoost MLT, provides gain in terms of both the modulation detection and complexity.
调制检测技术近年来受到了广泛关注，因为它们在军事和商业应用中非常重要，如软件定义无线电和认知无线电。现有的大多数调制检测算法仅针对非合作系统的检测。在这项工作中，我们提出了在存在空间相关信道和不完全信道状态信息（CSI）的情况下，用于 5G 通信的多中继协作多输入多输出（MIMO）系统中的调制检测。在目的节点，我们提取接收信号的高阶统计量作为判别特征。在应用主成分分析技术后，我们在低信噪比下对随机委员会和 AdaBoost 机器学习技术（MLT）进行了比较研究。使用真阳性率、假阳性率、精度、召回率、F-Measure 和构建模型所花费的时间等效率指标进行性能比较。仿真结果表明，与 AdaBoost MLT 相比，随机委员会 MLT 的使用在调制检测和复杂度方面都有收益。
