Research On Hybrid Service Scheduling Technology For For Industrial Internet Of Things

With the development of the fourth industrial revolution led by intelligent manufacturing,the types of services in the Industrial Internet of Things(IIoT)are gradually becoming more complex,and the number of terminal nodes is showing explosive growth.To support for higher requirements of latency,signaling overhead,and data rate,the communication scenarios where multiple services coexist is necessary to be studied.Under the strong support of the fifth generation(5G)communication technology for vertical industries,the IIoT can support ultra-reliable low latency communication(uRLLC)and enhanced mobile broadband(eMBB)services to achieve extremely high data rate,low latency with wide coverage,and relatively low power consumption.However,in actual industrial scenarios,the limitations of network time,energy,bandwidth,and computing resources will lead to conflicts in resource allocation between eMBB and uRLLC.In particular,the process of uRLLC preempting eMBB resources will lead to the degradation of eMBB’s service quality.In response to the above problems,this thesis focuses on the hybrid service scheduling technology for IIoT.The main contributions and innovation points are as follows:To solve the problem of signaling overhead caused by uRLLC in the preemption process of eMBB,the control channel overhead generated during the preemption process is studied.Then a hybrid scheduling scheme for the coexistence of eMBB and uRLLC services,which considers the optimization of additional control channel overhead,is proposed to achieve efficient coexistence of mixed services in data-intensive industrial Internet of Things.First,the generation of control channel overhead during preemption is analyzed.Then a mathematical problem of joint optimization of eMBB data rate,uRLLC reliability and additional control channel overhead is proposed.Second,to deal with the discreteness of optimization variables and the non-convexity of objective function,a solution based on AHP,coalitional game theory and matching algorithm is designed to solve the optimization problem.Finally,simulation verifies that the proposed scheme can achieve better performance compared with the comparison scheme in terms of eMBB data rate and uRLLC reliability with the reduced signal overhead.To solve the problem of the loss of eMBB performance caused by frequent preemption of widely connected uRLLC devices in the industrial Internet of Things,a hybrid scheduling scheme with resource reservation mechanism is designed.First,a multi-objective problem of joint optimization of eMBB data rate,uRLLC preemption times,and eMBB packet loss rate is proposed,with the goal to reducing the preemption behavior of uRLLC to eMBB.Second,in order to achieve dynamic resource reservation mechanism for the needs of real-time network in industrial scenarios more effectively,a deep reinforcement learning algorithm is introduced to determine the number of reserved resources.Furthermore,a channel-aware eMBB devices scheduling scheme and uRLLC devices scheduling scheme for optimal number of preemptions times are designed.Finally,the simulation results show that the proposed scheme can determine the appropriate number of reserved resources,so as to reduce the preemptive behavior effectively while meeting the performance requirements of uRLLC,with more efficient transmission of eMBB services.