Discussion on intelligent reflecting surface technology and its application in wireless blind spot coverage in coal mines
-
摘要: 针对现有无线通信技术在煤矿井下非视距场景中无线盲区覆盖所面临的难题，提出在煤矿井下无线通信系统中引入智能反射面（IRS）实现无线信号覆盖补盲的解决思路。分析了煤矿井下无线覆盖盲区问题的成因，包括封闭的矿井环境特征、普遍存在的非视距场景、发射功率与天线设置的安全约束。然而传统的煤矿井下无线覆盖盲区问题解决方案在硬件部署、维护成本、技术实现等方面存在局限性，无法真正满足矿山具体场景下的安全和高效通信需求。IRS因其低成本、低功耗、易部署和可扩展性等特点，在实现覆盖补盲方面具有性能优势。从硬件结构、辅助的信道模型和典型应用场景（精准定位、信能同传、无人机通信、边缘计算和物理层安全）3个方面介绍了IRS技术。提出了利用IRS技术优化井下无线盲区覆盖：通过在煤矿井下合理部署IRS，减少墙体对主要信号分量的吸收和散射，并利用IRS可调控的反射特性来优化信号的传播，以显著提高信号强度和覆盖范围。指出了IRS技术在煤矿井下无线通信中未来研究方向，包括IRS辅助的覆盖补盲系统的能量管理、基于人工智能的IRS辅助的井下通信、新型IRS技术应用和IRS灵活部署。Abstract: The existing wireless communication technologies in wireless blind spot coverage have challenges in non-line-of-sight scenarios in coal mines. In order to solve the above problems, a solution approach is proposed to introduce intelligent reflecting surface (IRS) into the wireless communication system in coal mines to achieve blind compensation of wireless signal coverage. This paper analyzes the causes of wireless coverage blind spots in coal mines, including closed mine environments characteristics, common non-line-of-sight scenarios, and safety constraints on transmission power and antenna settings. However, traditional wireless coverage blind spot solutions for underground coal mines have limitations in hardware deployment, maintenance costs, and technical implementation. They cannot truly meet safe and efficient communication needs of specific mining scenarios. IRS has significant performance advantages in achieving coverage blind compensation due to its low cost, low power consumption, easy deployment, and scalability. This paper introduces IRS technology from three aspects: hardware structure, auxiliary channel model and typical application scenarios (precise positioning, signal and energy simultaneous transmission, UAV communication, edge computing and physical layer security). This paper proposes the use of IRS technology to optimize underground wireless blind spot coverage. By deploying IRS reasonably in coal mines, it reduces the absorption and scattering of main signal components by walls, and utilizes the adjustable reflection characteristics of IRS to optimize signal propagation. It significantly improves signal strength and coverage range. The future research directions of IRS technology in underground wireless communication in coal mines are pointed out. The directions include energy management of IRS-aided coverage and blind compensation systems, IRS-aided underground communication based on artificial intelligence, application of new IRS technology, and flexible deployment of IRS.
-
-
[1] 杨帅. 煤矿井下智能化开采发展趋势[J]. 内蒙古煤炭经济,2023(3):160-162. DOI: 10.3969/j.issn.1008-0155.2023.03.054 YANG Shuai. Development trend of intelligent underground mining of coal mine[J]. Inner Mongolia Coal Economy,2023(3):160-162. DOI: 10.3969/j.issn.1008-0155.2023.03.054
[2] 申雪,刘驰,孔宁,等. 智慧矿山物联网技术发展现状研究[J]. 中国矿业,2018,27(7):120-125,143. DOI: 10.12075/j.issn.1004-4051.2018.07.031 SHEN Xue,LIU Chi,KONG Ning,et al. Research on the technical development status of the intelligent mine base on Internet of things[J]. China Mining Magazine,2018,27(7):120-125,143. DOI: 10.12075/j.issn.1004-4051.2018.07.031
[3] MA Long. Study on intelligent mine based on the application of 5G wireless communication system[J]. IOP Conference Series:Earth and Environmental Science,2020,588(3):032050. DOI: 10.1088/1755-1315/558/3/032050.
[4] 黄磊. 智慧化安全监测系统在矿山采矿工程中的应用[J]. 中国金属通报,2023(1):29-31. HUANG Lei. Application of intelligent safety monitoring system in mining engineering[J]. China Metal Bulletin,2023(1):29-31.
[5] 胡宏泽,杜志刚,储楠,等. 基于智慧矿山平台的人员定位系统关键技术[J]. 煤矿安全,2021,52(11):134-138. DOI: 10.13347/j.cnki.mkaq.2021.11.023 HU Hongze,DU Zhigang,CHU Nan,et al. Key technologies of personnel positioning system based on wisdom mine platform[J]. Safety in Coal Mines,2021,52(11):134-138. DOI: 10.13347/j.cnki.mkaq.2021.11.023
[6] FARJOW W,RAAHEMIFAR K,FERNANDO X. Novel wireless channels characterization model for underground mines[J]. Applied Mathematical Modelling,2015,39(19):5997-6007. DOI: 10.1016/j.apm.2015.01.043
[7] CHEN Kansong,WANG Chenqi,CHEN Liangqing,et al. Smart safety early warning system of coal mine production based on WSNs[J]. Safety Science,2020,124:104609. DOI: 10.1016/j.ssci.2020.104609.
[8] MABROUK I B,TALBI L,MNASRI B,et al. Experimental characterization of a wireless MIMO channel at 2.4 GHz in underground mine gallery[J]. Electromagnetics Research Letters,2012,29:97-106. DOI: 10.2528/PIERL11122904
[9] 史艳楠. 煤矿井下漏缆网络信道建模与故障诊断方法研究[D]. 北京: 中国矿业大学（北京）, 2018. SHI Yannan. Research on channel modeling and fault diagnosis of leaky coaxial cable network in underground coal mine[D]. Beijing: China University of Mining and Technology-Beijing, 2018.
[10] WU Qingqing,ZHANG Shuowen,ZHENG Beixiong,et al. Intelligent reflecting surface aided wireless communications:a tutorial[J]. IEEE Transactions on Communications,2021,69(5):3313-3351. DOI: 10.1109/TCOMM.2021.3051897
[11] VAN T,PHU H,IC P. IRS-aided wireless communication:from physics to channel modeling and characterization[J]. IEEE Access,2023,11:3184-3197. DOI: 10.1109/ACCESS.2023.3234762
[12] 齐峰,岳殿武,孙玉. 面向6G的智能反射面无线通信综述[J]. 移动通信,2022,46(4):65-73. DOI: 10.3969/j.issn.1006-1010.2022.04.012 QI Feng,YUE Dianwu,SUN Yu. A survey of intelligent reflecting surface wireless communications toward 6G[J]. Mobile Communications,2022,46(4):65-73. DOI: 10.3969/j.issn.1006-1010.2022.04.012
[13] KISSELEFF S,CHATZINOTAS S,OTTERSTEN B. Reconfigurable intelligent surfaces in challenging environments:underwater,underground,industrial and disaster[J]. IEEE Access,2021,9:150214-150233. DOI: 10.1109/ACCESS.2021.3125461
[14] RANJAN A,MISRA P,DWIVEDI B,et al. Studies on propagation characteristics of radio waves for wireless networks in underground coal mines[J]. Wireless Personal Communications,2017,97(2):2819-2832. DOI: 10.1007/s11277-017-4636-y
[15] RANJAN A, SAHU H B, MISRA P. Modeling and measurements for wireless communication networks in underground mine environments[J]. Measurement, 2020, 149. DOI: 10.1016/j.measurement.2019.106980.
[16] JAVAID F,WANG Anyi,SANA M U,et al. An optimized approach to channel modeling and impact of deteriorating factors on wireless communication in underground mines[J]. Sensors,2021,21(17):5905. DOI: 10.3390/s21175905
[17] GB/T 3836.1—2021爆炸性环境 第1部分: 设备 通用要求[S]. GB/T 3836.1-2021 Explosive atmospheres-Part 1: Equipment-General requirements[S].
[18] XU Jingjing,YANG Wei,ZHANG Linyuan,et al. Multi-sensor detection with particle swarm optimization for time-frequency coded cooperative WSNs based on MC-CDMA for underground coal mines[J]. Sensors,2015,15(9):21134-21152. DOI: 10.3390/s150921134
[19] WU Qingqing,ZHANG Rui. Towards smart and reconfigurable environment:intelligent reflecting surface aided wireless network[J]. IEEE Communications Magazine,2020,58(1):106-112. DOI: 10.1109/MCOM.001.1900107
[20] FENG Keming,WANG Qisheng,LI Xiao,et al. Deep reinforcement learning based intelligent reflecting surface optimization for MISO communication systems[J]. IEEE Wireless Communications Letters,2020,9(5):745-749. DOI: 10.1109/LWC.2020.2969167
[21] YANG Helin,XIONG Zehui,ZHAO Jun,et al. Deep reinforcement learning-based intelligent reflecting surface for secure wireless communications[J]. IEEE Transactions on Wireless Communications,2021,20(1):375-388. DOI: 10.1109/TWC.2020.3024860
[22] SUR S N,SINGH A K,KANDAR D,et al. Intelligent reflecting surface assisted localization:opportunities and challenges[J]. Electronics,2022,11(9):1411. DOI: 10.3390/electronics11091411.
[23] DARDARI D, DECARLI N, GUERRA A, et al. Localization in NLOS conditions using large reconfigurable intelligent surfaces[C]. IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications, Lucca, 2021. DOI: 10.1109/SPAWC51858.2021.9593241.
[24] WU Qingqing,GUAN Xinrong,ZHANG Rui. Intelligent reflecting surface-aided wireless energy and information transmission:an overview[J]. Proceedings of the IEEE,2022,110(1):150-170. DOI: 10.1109/JPROC.2021.3121790
[25] PAN Cunhua,REN Hong,WANG Kezhi,et al. Intelligent reflecting surface aided MIMO broadcasting for simultaneous wireless information and power transfer[J]. IEEE Journal on Selected Areas in Communications,2020,38(8):1719-1734. DOI: 10.1109/JSAC.2020.3000802
[26] LI Sixian,DUO Bin,YUAN Xiaojun,et al. Reconfigurable intelligent surface assisted UAV communication:joint trajectory design and passive beamforming[J]. IEEE Wireless Communications Letters,2020,9(5):716-720. DOI: 10.1109/LWC.2020.2966705
[27] LIU Yaqiong,PENG Mugen,SHOU Guochu,et al. Toward edge intelligence:multiaccess edge computing for 5G and Internet of things[J]. IEEE Internet of Things Journal,2020,7(8):6722-6747. DOI: 10.1109/JIOT.2020.3004500
[28] WANG Zhaoying,WEI Yifei,FENG Zhiyong,et al. Resource management and reflection optimization for intelligent reflecting surface assisted multi-access edge computing using deep reinforcement learning[J]. IEEE Transactions on Wireless Communications,2023,22(2):1175-1186. DOI: 10.1109/TWC.2022.3202948
[29] 吴振东,马建军,张玉萍,等. 太赫兹通信物理层安全技术发展研究[J]. 太赫兹科学与电子信息学报,2023,21(3):301-310. WU Zhendong,MA Jianjun,ZHANG Yuping,et al. Development of physical layer security communication in terahertz band[J]. Journal of Terahertz Science and Electronic Information Technology,2023,21(3):301-310.
[30] CUI Miao,ZHANG Guangchi,ZHANG Rui. Secure wireless communication via intelligent reflecting surface[J]. IEEE Wireless Communications Letters,2019,8(5):1410-1414. DOI: 10.1109/LWC.2019.2919685
[31] XIAO Liang,HONG Siyuan,XU Shiyu,et al. IRS-aided energy-efficient secure WBAN transmission based on deep reinforcement learning[J]. IEEE Transactions on Communications,2022,70(6):4162-4174. DOI: 10.1109/TCOMM.2022.3169813
[32] KUNSEI H,BIALKOWSKI K S,ALAM M S,et al. Improved communications in underground mines using reconfigurable antennas[J]. IEEE Transactions on Antennas and Propagation,2018,66(12):7505-7510. DOI: 10.1109/TAP.2018.2869250
[33] WU Qingqing,ZHANG Rui. Weighted sum power maximization for intelligent reflecting surface aided SWIPT[J]. IEEE Wireless Communications Letters,2020,9(5):586-590. DOI: 10.1109/LWC.2019.2961656
[34] WU Qingqing,ZHANG Rui. Joint active and passive beamforming optimization for intelligent reflecting surface assisted SWIPT under QoS constraints[J]. IEEE Journal on Selected Areas in Communications,2020,38(8):1735-1748. DOI: 10.1109/JSAC.2020.3000807
[35] ZARGARI S,KHALILI A,WU Qingqing,et al. Max-min fair energy-efficient beamforming design for intelligent reflecting surface-aided SWIPT systems with non-linear energy harvesting model[J]. IEEE Transactions on Vehicular Technology,2021,70(6):5848-5864. DOI: 10.1109/TVT.2021.3077477
[36] HUANG Chongwen,MO Ronghong,YUEN C. Reconfigurable intelligent surface assisted multiuser MISO systems exploiting deep reinforcement learning[J]. IEEE Journal on Selected Areas in Communications,2020,38(8):1839-1850. DOI: 10.1109/JSAC.2020.3000835
[37] ELBIR A M,COLERI S. Federated learning for channel estimation in conventional and RIS-assisted massive MIMO[J]. IEEE Transactions on Wireless Communications,2020,21(6):4255-4268.
[38] KANG Zhenyu, YOU Changsheng, ZHANG Rui. Active-IRS-aided wireless communication: fundamentals, designs and open issues[J/OL]. ArXiv, 2023. https://doi.org/10.48550/arXiv.2301.04311.
[39] KHOSHAFA M H,NGATCHED T M N,AHMED M H,et al. Active reconfigurable intelligent surfaces-aided wireless communication system[J]. IEEE Communications Letters,2021,25(11):3699-3703. DOI: 10.1109/LCOMM.2021.3110714
[40] ZHANG Hongliang,ZENG Shuhao,DI Boya,et al. Intelligent omni-surfaces for full-dimensional wireless communications:principles,technology,and implementation[J]. IEEE Communications Magazine,2022,60(2):39-45. DOI: 10.1109/MCOM.001.201097
[41] ZENG Shuhao,ZHANG Hongliang,DI Boya,et al. Reconfigurable intelligent surface (RIS) assisted wireless coverage extension:RIS orientation and location optimization[J]. IEEE Communications Letters,2021,25(1):269-273. DOI: 10.1109/LCOMM.2020.3025345
-
期刊类型引用(12)
1. 李梅，张鹏鹏，李元琛. AI赋能智能矿山:应用场景及未来展望. 煤炭经济研究. 2025(02): 161-169 . 
百度学术
2. 张美晨，张凯，赵丽娟，史百胜，王宇洋. 煤岩识别及采煤机自适应控制技术研究进展. 山西焦煤科技. 2023(12): 4-10 . 百度学术
3. 张强，张润鑫，刘峻铭，王聪，张赫哲，田莹. 煤矿智能化开采煤岩识别技术综述. 煤炭科学技术. 2022(02): 1-26 . 百度学术
4. 李彦明，孙利海. 基于多源异构信息耦合的煤岩界面识别技术研究. 矿业安全与环保. 2022(05): 6-10 . 百度学术
5. 陈家璘，周正，冯伟东，贺易，李静茹，赵世文. 一种无线传感器网络节点的故障检测算法. 计算技术与自动化. 2021(01): 38-42 . 百度学术
6. 窦国贤，高杨. 一种改进的电网信息系统自动化故障融合监测技术. 计算技术与自动化. 2020(01): 34-38 . 百度学术
7. 赵晓群，曹磊，张伍. 应用模块化+大数据处理技术提高计量新装接线率. 电子测量技术. 2020(05): 150-155 . 百度学术
8. 张林锋，田慕琴，宋建成，贺颖，冯君玲，刘西青. 基于多源数据融合的掘进机截割岩壁硬度识别方法. 振动与冲击. 2020(13): 7-15 . 百度学术
9. 窦国贤，高杨. 基于小波分析方法检测电网信息故障的研究. 计算机测量与控制. 2020(09): 38-41+57 . 百度学术
10. 王思. 基于多传感器的运动员训练信息融合分析系统设计. 计算技术与自动化. 2020(03): 140-146 . 百度学术
11. 王俊成，杜晓坤. 基于小波包分解和模糊神经网络的煤岩界面识别. 科技经济导刊. 2019(04): 48+36 . 百度学术
12. 刘忠超，刘勇军. 煤岩识别现状分析与发展方向. 南阳理工学院学报. 2018(04): 26-30 . 百度学术
其他类型引用(16)
下载:
计量
- 文章访问数: 937
- HTML全文浏览量: 120
- PDF下载量: 58
- 被引次数: 28
