工业物联网中URLLC的关键问题分析

doi:10.11959/j.issn.1000-0801.2022110

Abstract

Abstract:

In the age of 6G internet of things, massive terminal devices are connected intelligently to explore environmental information to improve life quality and create a more intelligent world.With the development of economic society and industry, the number of industrial nodes in the industrial internet of things (IIoT) grows at an significant speed.The real-time transmission and exchange of information between these industrial nodes puts forward higher requirements for communication delay and reliability.Under limited time-frequency resources and strict communication quality requirements, in order to ensure ultra-reliable and low-latency communication (URLLC) of massive terminals in IIoT, it is necessary to analyze the existing key problems from the aspects of low delay, reliability, energy efficiency, spectrum efficiency and scalability.In the scenario of IIoT, combined with the characteristics and requirements of URLLC, the key issues of communication in IIoT were studied, including challenges and opportunities in active user detection, random access, resource allocation and convergence of communication and computation.After summarizing and analyzing the key issues and existing technologies, the future research direction was proposed.

Key words: IIoT, URLLC, random access, resource allocation, convergence of communication and computation

CLC Number:

TN914

Han WANG, Lei DIAO, Mengling WANG, Xin RONG, Jiamin LI, Xiaohu YOU. A survey of key issues of URLLC in industrial internet of things[J]. Telecommunications Science, 2022, 38(Z1): 77-92.

Figures/Tables 5

References 91

[1]	ZHOU Y Q , LIU L , WANG L ,et al. Service-aware 6G:an intelligent and open network based on the convergence of communication,computing and caching[J]. Digital Communications and Networks, 2020,6(3): 253-260.
[2]	GONG Y K , YAO H P , WANG J J ,et al. Edge intelligence-driven joint offloading and resource allocation for future 6G industrial internet of things[J]. IEEE Transactions on Network Science and Engineering, 2022:1.
[3]	LI X M , XU L D . A review of internet of things—resource allocation[J]. IEEE Internet of Things Journal, 2021,8(11): 8657-8666.
[4]	YU X J , GUO H Q . A survey on IIoT security[C]// Proceedings of 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS). Piscataway:IEEE Press, 2019: 1-5.
[5]	LIN Z F , LIU J Q , XIAO J C ,et al. A survey:resource allocation technology based on edge computing in IIoT[C]// Proceedings of 2020 International Conference on Communications,Computing,Cybersecurity,and Informatics (CCCI). Piscataway:IEEE Press, 2020: 1-5.
[6]	POPOVSKI P , STEFANOVI? ? ,, NIELSEN J J ,et al. Wireless access in ultra-reliable low-latency communication (URLLC)[J]. IEEE Transactions on Communications, 2019,67(8): 5783-5801.
[7]	POKHREL S R , DING J , PARK J ,et al. Towards enabling critical mMTC:a review of URLLC within mMTC[J]. IEEE Access, 2020,8: 131796-131813.
[8]	DING J , NEMATI M , POKHREL S R ,et al. Enabling grant-free URLLC:an overview of principle and enhancements by massive MIMO[J]. IEEE Internet of Things Journal, 2022,9(1): 384-400.
[9]	SALH A , AUDAH L , SHAH N S M ,et al. A survey on deep learning for ultra-reliable and low-latency communications challenges on 6G wireless systems[J]. IEEE Access, 2021(9): 55098-55131.
[10]	VAEZI M , AZARI A , KHOSRAVIRAD S R ,et al. Cellular,wide-area,and non-terrestrial IoT:a survey on 5G advances and the road towards 6G[J]. IEEE Communications Surveys ＆ Tutorials,1028,PP(99):1
[11]	GUO M Q , GURSOY M C . Distributed sparse activity detection in cell-free massive MIMO systems[C]// Proceedings of 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). Piscataway:IEEE Press, 2019: 1-5.
[12]	MUMTAZ S , ALSOHAILY A , PANG Z B ,et al. Massive internet of things for industrial applications:addressing wireless IIoT connectivity challenges and ecosystem fragmentation[J]. IEEE Industrial Electronics Magazine, 2017,11(1): 28-33.
[13]	VILGELM M , MURAT GüRSU H , KELLERER W ,et al. Random access protocols for industrial internet of things:enablers,challenges,and research directions[EB]. 2021.
[14]	SHARMA S K , WANG X B . Toward massive machine type communications in ultra-dense cellular IoT networks:current issues and machine learning-assisted solutions[J]. IEEE Communications Surveys ＆ Tutorials, 2020,22(1): 426-471.
[15]	ZHAO X Y , CHEN W , POOR H V . Achieving extremely low-latency in industrial internet of things:joint finite blocklength coding,resource block matching,and performance analysis[J]. IEEE Transactions on Communications, 2021,69(10): 6529-6544.
[16]	POLYANSKIY Y , POOR H V , VERDU S . Channel coding rate in the finite blocklength regime[J]. IEEE Transactions on Information Theory, 2010,56(5): 2307-2359.
[17]	ZHOU Y Q , TIAN L , LIU L ,et al. Fog computing enabled future mobile communication networks:a convergence of communication and computing[J]. IEEE Communications Magazine, 2019,57(5): 20-27.
[18]	LIM G , JI H , SHIM B . Hybrid active user detection for massive machine-type communications in IoT[C]// Proceedings of 2018 International Conference on Information and Communication Technology Convergence (ICTC). Piscataway:IEEE Press, 2018: 1049-1052.
[19]	FENGLER A , HAGHIGHATSHOAR S , JUNG P ,et al. Non-Bayesian activity detection,large-scale fading coefficient estimation,and unsourced random access with a massive MIMO receiver[J]. IEEE Transactions on Information Theory, 2021,67(5): 2925-2951.
[20]	MEI Y K , GAO Z , WU Y P ,et al. Compressive sensing-based joint activity and data detection for grant-free massive IoT access[J]. IEEE Transactions on Wireless Communications, 2022,21(3): 1851-1869.
[21]	LIU L , YU W . Massive connectivity with massive MIMO—part I:d evice activity detection and channel estimation[J]. IEEE Transactions on Signal Processing, 2018,66(11): 2933-2946.
[22]	DJELOUAT H , LEINONEN M , JUNTTI M . Spatial correlation aware compressed sensing for user activity detection and chan nel estimation in massive MTC[J]. IEEE Transactions on Wireless Communications, 2022(99):1.
[23]	DING J , QU D M , LIU P ,et al. Machine learning enabled preamble collision resolution in distributed massive MIMO[J]. IEEE Transactions on Communications, 2021,69(4): 2317-2330.
[24]	SHAO X D , CHEN X M , NG D W K ,et al. Cooperative activity detection:sourced and unsourced massive random access paradigms[J]. IEEE Transactions on Signal Processing, 2020,68: 6578-6593.
[25]	XIAO J L , DENG G , NIE G F ,et al. Dynamic adaptive compressive sensing-based multi-user detection in uplink URLLC[C]// Proceedings of 2018 IEEE 29th Annual International Symposium on Personal,Indoor and Mobile Radio Communications. Piscataway:IEEE Press, 2018: 1-6.
[26]	WU L T , SUN P , WANG Z B ,et al. Joint user activity identification and channel estimation for grant-free NOMA:a spatial–temporal structure-enhanced approach[J]. IEEE Internet of Things Journal, 2021,8(15): 12339-12349.
[27]	SENEL K , LARSSON E G . Grant-free massive MTC-enabled massive MIMO:a compressive sensing approach[J]. IEEE Transactions on Communications, 2018,66(12): 6164-6175.
[28]	DJELOUAT H , LEINONEN M , RIBEIRO L ,et al. Joint user identification and channel estimation via exploiting spatial channel covariance in mMTC[J]. IEEE Wireless Communications Letters, 2021,10(4): 887-891.
[29]	CHEN Z L , SOHRABI F , YU W . Multi-cell sparse activity detection for massive random access:massive MIMO versus cooperative MIMO[J]. IEEE Transactions on Wireless Communications, 2019,18(8): 4060-4074.
[30]	SHAO X D , CHEN X M , QIANG Y Y ,et al. Feature-aided adaptive-tuning deep learning for massive device detection[J]. IEEE Journal on Selected Areas in Communications, 2021,39(7): 1899-1914.
[31]	HAGHIGHATSHOAR S , JUNG P , CAIRE G . Improved scaling law for activity detection in massive MIMO systems[C]// Proceedings of 2018 IEEE International Symposium on Information Theory. Piscataway:IEEE Press, 2018: 381-385.
[32]	WANG B C , DAI L L , MIR T ,et al. Multi-user detection based on structured compressive sensing for uplink grant-free NOMA[EB]. 2016.
[33]	SHAO X D , CHEN X M , JIA R D . A dimension reduction-based joint activity detection and channel estimation algorithm for massive access[J]. IEEE Transactions on Signal Processing, 2020,68: 420-435.
[34]	KE M L , GAO Z , WU Y P ,et al. Massive access in cell-free massive MIMO-based internet of things:cloud computing and edge computing paradigms[J]. IEEE Journal on Selected Areas in Communications, 2021,39(3): 756-772.
[35]	CHEN S F , ZHANG J Y , JIN Y ,et al. Wireless powered IoE for 6G:massive access meets scalable cell-free massive MIMO[J]. China Communications, 2020,17(12): 92-109.
[36]	CHENG L , LIU L , CUI S G . A covariance-based user activity detection and channel estimation approach with novel pilot design[C]// Proceedings of 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications. Piscataway:IEEE Press, 2020: 1-5.
[37]	CUI Y , LI S C , ZHANG W Q ,et al. Jointly sparse signal recovery and support recovery via deep learning with applications in MIMO-based grant-free random access[EB]. 2020.
[38]	ZHANG J , HE H T , WEN C K ,et al. Deep learning based on orthogonal approximate message passing for CP-free OFDM[C]// Proceedings of ICASSP 2019 - 2019 IEEE International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE Press, 2019: 8414-8418.
[39]	LIU J , AGIWAL M , QU M ,et al. Online control of preamble groups with priority in massive IoT networks[J]. IEEE Journal on Selected Areas in Communications, 2021,39(3): 700-713.
[40]	ASTUDILLO C A , DE ANDRADE T P C , DA FONSECA N L S . Allocation of control resources with preamble priority awareness for human and machine type communications in LTE-Advanced networks[C]// Proceedings of 2017 IEEE International Conference on Communications. Piscataway:IEEE Press, 2017: 1-6.
[41]	ASTUDILLO C A , DE ANDRADE T P C , DA FONSECA N L S . Impact of preamble-priority-aware downlink control signaling scheduling on LTE/LTE-A network performance[C]// Proceedings of 2017 IEEE 86th Vehicular Technology Conference. Piscataway:IEEE Press, 2017: 1-5.
[42]	VILGELM M , GüRSU H M ,, KELLERER W ,et al. LATMAPA:load-adaptive throughput-maximizing preamble allocation for prioritization in 5G random access[J]. IEEE Access, 2017(5): 1103-1116.
[43]	VANGELISTA L , CENTENARO M . Performance evaluation of HARQ schemes for the internet of things[J]. Computers, 2018,7(4): 48.
[44]	SESIA S , TOUFIK I , BAKER M . LTE - the UMTS long term evolution[M]. Chichester,UK: John Wiley ＆ Sons,Ltd., 2011.
[45]	MAKKI B , SVENSSON T , CAIRE G ,et al. Fast HARQ over finite blocklength codes:a technique for low-latency reliable communication[J]. IEEE Transactions on Wireless Communications, 2019,18(1): 194-209.
[46]	G?KTEPE B , RYKOVA T , FEHRENBACH T ,et al. Feedback prediction for proactive HARQ in the context of industrial internet of things[C]// Proceedings of GLOBECOM 2020 - 2020 IEEE Global Communications Conference. Piscataway:IEEE Press, 2020: 1-7.
[47]	DE CARVALHO E , BJORNSON E , SORENSEN J H ,et al. Random access protocols for massive MIMO[J]. IEEE Communications Magazine, 2017,55(5): 216-222.
[48]	DE CARVALHO E , BJ?RNSON E , S?RENSEN J H ,et al. Random pilot and data access in massive MIMO for machine-type communications[J]. IEEE Transactions on Wireless Communications, 2017,16(12): 7703-7717.
[49]	BJ?RNSON E , DE CARVALHO E , S?RENSEN J H , ,et al. A random access protocol for pilot allocation in crowded massive MIMO systems[J]. IEEE Transactions on Wireless Communications, 2017,16(4): 2220-2234.
[50]	DING J , QU D M , JIANG H ,et al. Virtual carrier sensing-based random access in massive MIMO systems[J]. IEEE Transactions on Wireless Communications, 2018,17(10): 6590-6600.
[51]	BJ?RNSON E , DE CARVALHO E , LARSSON E G ,et al. Random access protocol for massive MIMO:strongest-user collision resolution (SUCR)[C]// Proceedings of 2016 IEEE International Conference on Communications. Piscataway:IEEE Press, 2016: 1-6.
[52]	JIANG H , QU D M , DING J ,et al. Multiple preambles for high success rate of grant-free random access with massive MIMO[J]. IEEE Transactions on Wireless Communications, 2019,18(10): 4779-4789.
[53]	BAI L , LIU J X , YU Q ,et al. A collision resolution protocol for random access in massive MIMO[J]. IEEE Journal on Selected Areas in Communications, 2021,39(3): 686-699.
[54]	SOLTANMOHAMMADI E , GHAVAMI K , NARAGHI-POUR M , . A survey of traffic issues in machine-to-machine communications over LTE[J]. IEEE Internet of Things Journal, 2016,3(6): 865-884.
[55]	PAN Q , WEN X M , LU Z M ,et al. Cluster-based group paging for massive machine type communications under 5G networks[J]. IEEE Access, 2018,6: 64891-64904.
[56]	HE Y X , REN G L , LIANG S . Spatial group based access class barring for massive access in M2M communications[J]. IEEE Communications Letters, 2021,25(3): 812-816.
[57]	ALTHUMALI H D , OTHMAN M , NOORDIN N K ,et al. Dynamic backoff collision resolution for massive M2M random access in cellular IoT networks[J]. IEEE Access, 2020(8): 201345-201359.
[58]	ALI M S , HOSSAIN E , KIM D I . LTE/LTE-A random access for massive machine-type communications in smart cities[J]. IEEE Communications Magazine, 2017,55(1): 76-83.
[59]	MIUCCIO L , PANNO D , RIOLO S . Joint congestion control and resource allocation for massive MTC in 5G networks based on SCMA[C]// Proceedings of 2019 15th International Conference on Telecommunications (ConTEL). Piscataway:IEEE Press, 2019: 1-8.
[60]	YU Y , YUAN L Y . Study on slotted random multi-access protocol with two-dimensional probability[C]// Proceedings of 2019 IEEE International Conference on Computer Science and Educational Informatization. Piscataway:IEEE Press, 2019: 244-250.
[61]	CHENG M Y , LIN G Y , WEI H Y ,et al. Overload control for machine-type-communications in LTE-advanced system[J]. IEEE Communications Magazine, 2012,50(6): 38-45.
[62]	TANG F X , KAWAMOTO Y , KATO N ,et al. Future intelligent and secure vehicular network toward 6G:machine-learning approaches[J]. Proceedings of the IEEE, 2020,108(2): 292-307.
[63]	CHEN Z Q , SMITH D B . Heterogeneous machine-type communications in cellular networks:random access optimization by deep reinforcement learning[C]// Proceedings of 2018 IEEE International Conference on Communications. Piscataway:IEEE Press, 2018: 1-6.
[64]	MOON J , LIM Y . Access control of MTC devices using reinforcement learning approach[C]// Proceedings of 2017 International Conference on Information Networking (ICOIN). Piscataway:IEEE Press, 2017: 641-643.
[65]	BELLO L M , MITCHELL P , GRACE D . Application of Q-learning for RACH access to support M2M traffic over a cellular network[C]// Proceedings of European Wireless 2014;20th European Wireless Conference,VDE2014: 1-6.
[66]	MOHAMMED A H , KHWAJA A S , ANPALAGAN A ,et al. Base station selection in M2M communication using Q-learning algorithm in LTE-A networks[C]// Proceedings of 2015 IEEE 29th International Conference on Advanced Information Networking and Applications. Piscataway:IEEE Press, 2015: 17-22.
[67]	SHI Y , SAGDUYU Y E , ERPEK T . Reinforcement learning for dynamic resource optimization in 5G radio access network slicing[C]// Proceedings of 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks. Piscataway:IEEE Press, 2020: 1-6.
[68]	胡江祺 . 工业物联网中动态资源分配机制的研究[D]. 西安:西安电子科技大学, 2019.
	HU J Q . Research on dynamic resource allocation scheme in industrial Internet of Things[D]. Xi'an:Xidian University, 2019.
[69]	REN H , PAN C H , DENG Y S ,et al. Joint pilot and payload power allocation for massive-MIMO-enabled URLLC IIoT networks[J]. IEEE Journal on Selected Areas in Communications, 2020,38(5): 816-830.
[70]	SHE C Y , YANG C Y , QUEK T Q S . Radio resource management for ultra-reliable and low-latency communications[J]. IEEE Communications Magazine, 2017,55(6): 72-78.
[71]	SHE C Y , YANG C Y , QUEK T Q S . Joint uplink and downlink resource configuration for ultra-reliable and low-latency communications[J]. IEEE Transactions on Communications, 2018,66(5): 2266-2280.
[72]	LIBRINO F , SANTI P . Resource allocation and sharing in URLLC for IoT applications using shareability graphs[J]. IEEE Internet of Things Journal, 2020,7(10): 10511-10526.
[73]	PAN C H , REN H , DENG Y S ,et al. Joint blocklength and location optimization for URLLC-enabled UAV relay systems[J]. IEEE Communications Letters, 2019,23(3): 498-501.
[74]	REN H , PAN C H , DENG Y S ,et al. Resource allocation for URLLC in 5G mission-critical IoT networks[C]// Proceedings of ICC 2019 - 2019 IEEE International Conference on Communications. Piscataway:IEEE Press, 2019: 1-6.
[75]	REN H , PAN C H , DENG Y S ,et al. Joint power and blocklength optimization for URLLC in a factory automation scenario[J]. IEEE Transactions on Wireless Communications, 2019,19(3): 1786-1801.
[76]	SUN C J , SHE C Y , YANG C Y ,et al. Optimizing resource allocation in the short blocklength regime for ultra-reliable and low-latency communications[J]. IEEE Transactions on Wireless Communications, 2019,18(1): 402-415.
[77]	ZHAO J , ZHU W P . Power allocation and scaling law analysis for secured and energy efficient URLLC IoT networks[J]. Physical Communication, 2021,45:101293.
[78]	CHALAPATHI G S S , CHAMOLA V , VAISH A ,et al. Industrial Internet of Things (IIoT) Applications of Edge and Fog Computing:A Review and Future DirectionsFog/Edge Computing for Security,Privacy,and Applications, 2021: 293-325.
[79]	QIU T , CHI J C , ZHOU X B ,et al. Edge computing in industrial Internet of Things:architecture,advances and challenges[J]. IEEE Communications Surveys ＆ Tutorials, 2020,22(4): 2462-2488.
[80]	SATYANARAYANAN M , BAHL P , CACERES R ,et al. The case for VM-based cloudlets in mobile computing[J]. IEEE Pervasive Computing, 2009,8(4): 14-23.
[81]	WANG S , ZHANG X , ZHANG Y ,et al. A survey on mobile edge networks:convergence of computing,caching and communications[J]. IEEE Access, 2017,5: 6757-6779.
[82]	EL-SAYED H , SANKAR S , PRASAD M ,et al. Edge of things:the big picture on the integration of edge,IoT and the cloud in a distributed computing environment[J]. IEEE Access, 2018,6: 1706-1717.
[83]	MARKAKIS E K , KARRAS K , SIDERIS A ,et al. Computing,caching,and communication at the edge:the cornerstone for building a versatile 5G ecosystem[J]. IEEE Communications Magazine, 2017,55(11): 152-157.
[84]	XU H S , WU J , LI J H ,et al. Deep-reinforcement-learning-based cybertwin architecture for 6G IIoT:an integrated design of control,communication,and computing[J]. IEEE Internet of Things Journal, 2021,8(22): 16337-16348.
[85]	KEKKI S , FEATHERSTONE W , FANG Y ,et al. MEC in 5G networks[EB]. 2018.
[86]	HOU X W , REN Z Y , YANG K ,et al. IIoT-MEC:a novel mobile edge computing framework for 5G-enabled IIoT[C]// Proceedings of 2019 IEEE Wireless Communications and Networking Conference. Piscataway:IEEE Press, 2019: 1-7.
[87]	YANG B , CAO X L , LI X F ,et al. Mobile-edge-computing-based hierarchical machine learning tasks distribution for IIoT[J]. IEEE Internet of Things Journal, 2020,7(3): 2169-2180.
[88]	ZHAO Z C , ZHAO R , XIA J J ,et al. A novel framework of three-hierarchical offloading optimization for MEC in industrial IoT networks[J]. IEEE Transactions on Industrial Informatics, 2020,16(8): 5424-5434.
[89]	CHEN W H , ZHANG Z , HONG Z C ,et al. Cooperative and distributed computation offloading for blockchain-empowered industrial Internet of Things[J]. IEEE Internet of Things Journal, 2019,6(5): 8433-8446.
[90]	CHEN Y , LIU Z Y , ZHANG Y C ,et al. Deep reinforcement learning-based dynamic resource management for mobile edge computing in industrial Internet of Things[J]. IEEE Transactions on Industrial Informatics, 2021,17(7): 4925-4934.
[91]	WU J J , ZHANG G L , NIE J Q ,et al. Deep reinforcement learning for scheduling in an edge computing-based industrial Internet of Things[J]. Wireless Communications and Mobile Computing,2021, 2021:8017334.

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算法	文献	举例	优点	缺点
协方差算法	[28]	梯度下降算法	所需导频较短	需要大量天线
CS算法	[20]	AMP、MAP	估计精度高	复杂度较高
贝叶斯算法	[22]	SBL	估计精度高	信道信息已知
深度学习算法	[37]	基于模型或数据方法	计算复杂度低	需要大量数据

RA方案	接入时延	接入成功率	能量效率	QoS保障
ACB方案	不定	较高	中等	较高
退避方案	较低	较低	较低	无
动态资源分配方案	中等	中等	中等	无
时隙随机接入方案	较低	高	高	较低
RA资源分割方案	较高	中等	中等	无
基于寻呼的方案	中等	中等	中等	无

序号	文献	优化目标	约束条件	优化算法	优势和不足
1	[69]	最大化设备加权和速率	时延、用户QoS	几何程序问题求解	算法收敛快，但对有限块长的速率表达式和SINR表达式进行了近似估计带来误差
2	[71]	最小化上行和下行总带宽	端到端时延和丢包率	两步算法	联合考虑上行和下行传输，但只考虑了本地通信场景下的优化
3	[72]	最大化频谱效率	可靠性、时延	迭代算法	针对可用频谱分布在不连续的频段这一问题，提高了系统频谱效率和用户公平性，但只适用于周期的数据包到达模型
4	[73]	最小化错误概率	时延	基于扰动的迭代算法	算法复杂度低，但考虑的无人机辅助中继的特殊场景，无人机本身的续航能力、计算和通信能力有限
5	[74-75]	最小化错误概率	可靠性、能量、时延	图论和迭代算法	对4种下行多址传输策略的资源分配进行了优化，但是在设备数多于两个时优化算法的可拓展性还有待提高
6	[76]	最大化能量效率	时延、解码错误概率	非凸问题转化	提升了的URLLC的能量效率，但是假设没有强的小区内/间干扰
8	[77]	最大化安全能量效率	最小安全可达速率	凸优化	考虑了URLLC中的安全性问题，但使用凸优化的方法复杂度较高

文献	优化目标	方法
[87]	最小化任务时延	机器学习
[88]	降低时延和能耗	3层优化框架
[89]	最小化运营成本	分布式算法
[90]	最小化任务的长期平均时延	深度强化学习
[91]	提高服务质量	深度强化学习

A survey of key issues of URLLC in industrial internet of things

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