基于高阶累积量和DNN模型的井下信号识别方法
Underground signal recognition method based on higher-order cumulants and DNN model
【索引】王安义,李立.基于高阶累积量和DNN模型的井下信号识别方法[J].工矿千亿国际app下载,2020,46(2):82-87.
【Reference】WANG Anyi,LI Li.Underground signal recognition method based on higher-order cumulant and DNN model[J].Industry and Mine Automation,2020,46(2):82-87.
【DOI】10.13272/j.issn.1671-251x.2019100064
【作者】 王安义,李立
【Author】 WANG Anyi,LI Li
【作者机构】西安科技大学 通信与信息工程学院, 陕西 西安710054
【Unit】College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China
【摘要】针对矿井复杂异构的无线环境,提出一种基于高阶累积量和DNN模型的井下信号识别方法,实现了井下BPSK,QPSK,8PSK,2FSK,4FSK,8FSK,32QAM,64QAM,OFDM等数字信号的自动调制识别。分析得到9种数字信号的高阶累积量理论值,并通过傅里叶变换提高信号辨识度;分析井下小尺度衰落信道对高阶累积量的影响,推导出经过井下衰落信道后信号的高阶累积量计算表达式,根据高阶累积量理论值构造特征参数并训练DNN模型,实现信号识别。仿真分析结果表明,该方法在矿井Nakagami-m衰落信道下有出色的调制识别性能,信噪比为-5 dB时平均正确识别率为89.2%以上,信噪比为5 dB以上时平均正确识别率为100%。该方法为在特殊复杂环境下的信号识别检测提供了新思路。
【Abstract】In view of complex and heterogeneous wireless environment of mine, an underground signal recognition method based on higher-order cumulants and DNN model was proposed to realize automatic modulation recognition of underground digital signals of BPSK, QPSK, 8PSK, 2FSK, 4FSK, 8FSK, 32QAM, 64QAM, OFDM. Theoretical values of high-order cumulants of the 9 kinds of digital signals were obtained by analysis, and the signal identification was improved by Fourier transform. The influence of underground small-scale fading channels on high-order cumulants were analyzed, high-order cumulants calculation expression of the signal after passing through the underground channel was derived, and signal recognition was realized using characteristic parameters constructed according high-order cumulants to train DNN model. The simulation analysis results show that the method has excellent modulation recognition performance in mine Nakagami-m fading channel, average correct recognition rate is more than 89.2% when the signal-to-noise ratio is -5 dB, and the average correct recognition rate is 100% when the signal-to-noise ratio is 5 dB or more. The method provides a new idea for signal recognition and detection in special and complex environments.
【关键词】 矿井通信; 井下信号识别; Nakagami-m衰落信道; 高阶累积量; 深度神经网络; DNN模型
【Keywords】mine communication; underground signal recognition; Nakagami-m fading channel; higher-order cumulant; deep neural network; DNN model
【文献出处】工矿千亿国际app下载,2020年2期
【基金】国家重点产业创新链项目(2019ZDLSF07-06);国家自然科学基金青年科学基金项目(61801372)
【分类号】TD655
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