| In recent years,with the continuous development of mobile communication technology,network technology and sensing technology,the Internet of Things(IoT)has become an important part of the new generation of information technology,which has achieved large-scale industrial development in vertical applications such as smart grid,smart transportation,smart medical,and smart industry.The emergence of 5G Industrial IoT(IIoT)provides key technical support for the realization of Industry 4.0,which will realize the transformation of production methods and promote the transformation of traditional production methods to green,intelligent,and low-carbon directions.The mode of production is changed to a flexible production mode,which will greatly improve production efficiency.However,with the rapid development of 5G IIoT communication technology and the continuous expansion of network scale,the massive data generated by various services has increased the load of the core network,which makes it difficult for limited network resources to satisfy the quality of service(QoS)requirements such as massive connectivity,low latency,low power consumption,and high reliability.On the other hand,the dramatic increase in the energy consumption of wireless networks has exacerbated the problem of carbon dioxide emissions in the atmosphere,which results in unbearable economic and ecological costs.Therefore,orienting to the industry vision of "clean,low-carbon,high-efficiency,and safety" for the 5G IIoT,this thesis studies the 5G IIoT green wireless and energy resources collaborative management technology with low ener-gy consumption and high energy efficiency,which aims to comprehensively optimize the utilization efficiency of network resources.The goal of this thesis is to build a 5G IIoT with low energy consumption,high energy efficiency,and green and sustainable development,comprehensively improve economic and ecological benefits,and support the realization of carbon peaking and carbon neutrality goals.The main work and innovations of this thesis are summarized as follows:(1)The Multi-Dimension Resource Joint Optimization for Edge Computing-Based 5G IIoTFor the edge computing-based industrial IoT scenario,considering the difficulty of long-term green wireless and energy resources(including channel allocation,power control,and computing resource allocation)collaborative management optimization,in order to reduce the energy consumption of IIoT devices and improve the system throughput,this thesis proposes a multi-dimension resource joint optimization algorithm with queue-aware based on Lyapunov optimization,price matching and Lagrange multiplier method.The proposed algorithm can dynamically adjust channel allocation,power control and computing resource allocation strategies according to real-time channel state information and queue backlog information to ensure throughput requirements and data queue stability,and significantly reduce the total energy consumption of all IIoT devices.Furthermore,the proposed algorithm can satisfy the strict communication and data processing requirements of a variety of emerging services.(2)Multi-Timescale Resource Allocation for 5G IIoT with Massive ConnectivityIn order to solve the contradiction between the massive connectivity demands and the limited spectrum resources caused by the explosive growth of the number of IIoT devices in the future,this thesis introduces the non-orthogonal multiple access(NOMA)technology into the edge computing-based IIoT scenario.However,while greatly improving the spectral efficiency,the utilizing of NOMA technology also complicates green wireless and energy resources collaborative management in the massive connectivity scenario.Therefore,this thesis proposes a multi-timescale and multi-dimension resource allocation and task splitting algorithm.Through the joint optimization of resource block allocation,task splitting and local computing resources,the proposed algorithm can further reduce the energy consumption and task delay of IIoT devices and support the massive access requirements of massive IIoT devices.Furthermore,The proposed algorithm has great scalability and low complexity,which is suitable for a variety of IIoT low-power and large-connection scenarios.(3)Resource Allocation for Large-Scale Multi-Antenna Systems and Energy Harvesting-Based 5G IIoTFocused on the problems of high economic and ecological cost caused by huge energy consumption on the base station side,this thesis applies the energy harvesting technology to the large-scale multi-antenna systems-based IIoT scenario.Considering that the huge energy consumption of 5G base stations also brings higher transmission rates and system capacity,it is not objective to only use energy consumption as a performance evaluation matrix,and the dynamic changes of renewable energy arrival information and electricity price information exacerbate the complexity of green wireless and energy resources collaborative management at the base station side.Therefore,this thesis proposes an online iterative resource allocation joint optimization algorithm based on nonlinear fractional programming,bisection method and Lyapunov optimization theory.Through the joint optimization of antenna selection and power control,the proposed algorithm can satisfy the differentiated communication requirements of IIoT services while maximize the long-term energy economic efficiency. |
