| Citation: |
ZHANG Liya, MA Zheng, HAO Bonan, et al. Interference monitoring technology for mine-used 5G communication signal transmission[J]. Journal of Mine Automation，2024，50（11）：62-69. DOI: 10.13272/j.issn.1671-251x.204090054
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Compared to conventional interference suppression techniques, such as adaptive filtering and adaptive interference cancellation, blind source separation (BSS) offers the advantage of separating multiple mixed signals with lower computational complexity and higher robustness. However, BSS has limitations in addressing the complex and dynamic interference sources found in underground mining environments. It also lacks automated mechanisms for analyzing and evaluating processed signal components, which not only hampers communication efficiency but may also lead to safety risks due to residual interference. To overcome these challenges, this study proposed a neural network-based interference monitoring and suppression method tailored for mine-used 5G communication signal transmission. By analyzing the characteristics of interference sources in key areas such as main haulage roadways, fully mechanized mining faces, and substations, the study identified the suppression and mitigation of spike interference and crosstalk signals as critical for 5G anti-interference performance. The proposed method initially employed BSS for the preliminary separation of interference components in mine-used 5G communication signals. It then leveraged a neural network for feature extraction and deep analysis of the separated signals, enabling precise identification and quantification of residual interference. If the monitored interference signal exceeded a preset threshold, the system automatically triggered a new round of suppression, forming an iterative and optimized closed-loop control process. Experimental results revealed that: In a 100 MHz full-bandwidth transmission environment, the proposed method achieved a suppression gain of 13 dB for both spike interference and crosstalk signals, showing improvements of approximately 117% and 86%, respectively, over BSS-based interference suppression alone. Compared to traditional techniques, such as BSS, the proposed method enhanced the signal-to-noise ratio (SNR) by an average of 15.56% and reduced the bit error rate (BER) by an average of 21.88%, which could significantly improve signal quality.
| [1] |
孙继平. 煤矿智能化与矿用5G[J]. 工矿自动化,2020,46(8):1-7.
SUN Jiping. Coal mine intelligence and mine-used 5G[J]. Industry and Mine Automation,2020,46(8):1-7.
|
| [2] |
孙继平. 煤矿智能化与矿用5G和网络硬切片技术[J]. 工矿自动化,2021,47(8):1-6.
SUN Jiping. Coal mine intelligence,mine 5G and network hard slicing technology[J]. Industry and Mine Automation,2021,47(8):1-6.
|
| [3] |
李晨鑫. 煤矿用5G关键技术研究现状与发展方向[J]. 工矿自动化,2024,50(7):79-88.
LI Chenxin. Research status and development direction of 5G key technologies for coal mines[J]. Journal of Mine Automation,2024,50(7):79-88.
|
| [4] |
康辉,窦文章,文志成. 技术变革视角下移动通信代内创新和代际演进及未来发展展望[J]. 创新科技,2023,23(4):81-92.
KANG Hui,DOU Wenzhang,WEN Zhicheng. Intra-generation innovation and inter-generation evolution of mobile communication and its future development prospects in the perspective of technological change[J]. Innovation Science and Technology,2023,23(4):81-92.
|
| [5] |
王子陵,高晓成,杜胜利,等. 矿用F5G架构的智能矿井建设[J]. 有色金属工程,2024,14(10):170.
WANG Ziling,GAO Xiaocheng,DU Shengli,et al. Intelligent mine construction based on F5G architecture for mining[J]. Nonferrous Metals Engineering,2024,14(10):170.
|
| [6] |
徐天河,王森,代培培. UWB/INS紧组合变分贝叶斯自适应滤波算法[J/OL]. 导航定位学报:1-12[2024-09-21]. https://kns.cnki.net/kcms/detail/10.1096.P.20241121.1557.002.html.
XU Tianhe,WANG Sen,DAI Peipei. UWB/INS tightly coupled integration algorithm based on variational Bayesian adaptive Kalman filter[J/OL]. Journal of Navigation and Positioning:1-12[2024-09-21]. https://kns.cnki.net/kcms/detail/10.1096.P.20241121.1557.002.html.
|
| [7] |
刘建成,全厚德,赵宏志,等. 基于迭代变步长LMS的数字域自干扰对消[J]. 电子学报,2016,44(7):1530-1538. DOI: 10.3969/j.issn.0372-2112.2016.07.002
LIU Jiancheng,QUAN Houde,ZHAO Hongzhi,et al. Digital self-interference cancellation based on iterative variable step-size LMS[J]. Acta Electronica Sinica,2016,44(7):1530-1538. DOI: 10.3969/j.issn.0372-2112.2016.07.002
|
| [8] |
李哲宇,李亚星,张嘉毫,等. 非合作干扰对消技术中空间分辨率建模与分析[J]. 国防科技大学学报,2024,46(5):45-53. DOI: 10.11887/j.cn.202405006
LI Zheyu,LI Yaxing,ZHANG Jiahao,et al. Modeling and analysis of spatial resolution in noncooperative interference cancellation technique[J]. Journal of National University of Defense Technology,2024,46(5):45-53. DOI: 10.11887/j.cn.202405006
|
| [9] |
解元,邹涛,孙为军,等. 面向卷积混叠环境下的盲源分离新方法[J]. 自动化学报,2023,49(5):1062-1072.
XIE Yuan,ZOU Tao,SUN Weijun,et al. Novel blind source separation method for convolutive mixed environment[J]. Acta Automatica Sinica,2023,49(5):1062-1072.
|
| [10] |
孙玉伟,罗林根,陈敬德,等. 含噪背景下基于盲源分离与NSVDD的断路器机械故障诊断方法[J]. 高电压技术,2022,48(3):1104-1112.
SUN Yuwei,LUO Lingen,CHEN Jingde,et al. Mechanical fault diagnosis method of circuit breaker based on blind source separation and NSVDD under noisy background[J]. High Voltage Engineering,2022,48(3):1104-1112.
|
| [11] |
冯平兴,张洪波,李文翔. 噪声中的复信号盲源分离算法[J]. 电子技术应用,2022,48(4):67-70,75.
FENG Pingxing,ZHANG Hongbo,LI Wenxiang. Blind source separation algorithm for complex signals in noise[J]. Application of Electronic Technique,2022,48(4):67-70,75.
|
| [12] |
张延良,张玉,张伟涛. 改进的带参考信号盲源分离算法[J]. 科学技术与工程,2022,22(6):2311-2316. DOI: 10.3969/j.issn.1671-1815.2022.06.021
ZHANG Yanliang,ZHANG Yu,ZHANG Weitao. Improved blind source separation algorithm with reference signal[J]. Science Technology and Engineering,2022,22(6):2311-2316. DOI: 10.3969/j.issn.1671-1815.2022.06.021
|
| [13] |
陈明虎, 李程, 涂刚毅, 等. 通信干扰信号分类识别方法综述[J]. 电子信息对抗技术,2024,39(3):86-94.
CHEN Minghu, LI Cheng, TU Gangyi, et al. An overview of classification and recognition method of communication jamming signals[J]. Electronic Information Warfare Technology,2024,39(3):86-94.
|
| [14] |
张立亚,杨维,李晋豫. 矿用5G通信系统射频能量损耗模型[J]. 华中科技大学学报(自然科学版),2021,49(9):6-10,29.
ZHANG Liya,YANG Wei,LI Jinyu. Radio frequency energy loss model of mine 5G communication system[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition),2021,49(9):6-10,29.
|
| [15] |
李俊卿,刘静. 结合卷积神经网络和迁移学习的电机轴承故障诊断方法[J]. 华北电力大学学报(自然科学版),2023,50(1):76-83,91. DOI: 10.3969/j.ISSN.1007-2691.2023.01.09
LI Junqing,LIU Jing. Fault diagnosis method of motor bearing based on CNN and transfer learning[J]. Journal of North China Electric Power University (Natural Science Edition),2023,50(1):76-83,91. DOI: 10.3969/j.ISSN.1007-2691.2023.01.09
|
| [16] |
周飞燕,金林鹏,董军. 卷积神经网络研究综述[J]. 计算机学报,2017,40(6):1229-1251. DOI: 10.11897/SP.J.1016.2017.01229
ZHOU Feiyan,JIN Linpeng,DONG Jun. Review of convolutional neural network[J]. Chinese Journal of Computers,2017,40(6):1229-1251. DOI: 10.11897/SP.J.1016.2017.01229
|
| [17] |
林景栋,吴欣怡,柴毅,等. 卷积神经网络结构优化综述[J]. 自动化学报,2020,46(1):24-37.
LIN Jingdong,WU Xinyi,CHAI Yi,et al. Structure optimization of convolutional neural networks:a survey[J]. Acta Automatica Sinica,2020,46(1):24-37.
|
| [18] |
刘赟. ReLU激活函数下卷积神经网络的不同类型噪声增益研究[D]. 南京:南京邮电大学,2023.
LIU Yun. Research on different types of noise gains of convolutional neural networks under ReLU activation function[D]. Nanjing:Nanjing University of Posts and Telecommunications,2023.
|
| [19] |
靳晶晶,王佩. 基于卷积神经网络的图像识别算法研究[J]. 通信与信息技术,2022(2):76-81.
JIN Jingjing,WANG Pei. Research on image recognition algorithm based on convolutional neural network[J]. Communication & Information Technology,2022(2):76-81.
|
| [20] |
张立亚. 基于动目标特征提取的矿井目标监测[J]. 煤炭学报,2017,42(增刊2):603-610.
ZHANG Liya. Mine target monitoring based on feature extraction of moving target[J]. Journal of China Coal Society,2017,42(S2):603-610.
|
| [21] |
张立亚. 全矿井融合通信系统研究[J]. 工矿自动化,2018,44(3):12-16.
ZHANG Liya. Research on mine integrated communication system[J]. Industry and Mine Automation,2018,44(3):12-16.
|
| [22] |
冯登国,徐静,兰晓. 5G移动通信网络安全研究[J]. 软件学报,2018,29(6):1813-1825.
FENG Dengguo,XU Jing,LAN Xiao. Study on 5G mobile communication network security[J]. Journal of Software,2018,29(6):1813-1825.
|
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