Research On Computing Resource Allocation And Energy Efficiency Optimization Of Emergency Uavs Based On Two-ray Propagation Model

In recent years,with the rapid development of Internet of Things technology and artificial intelligence,Unmanned Aerial Vehicle assisted Mobile edge computing(UAV-assisted MEC)has become a research hotspot and important application in the field of communication.UAV-assisted MEC has the characteristics of easy scheduling,high sensitivity,low cost,and low computational delay,which can effectively solve the problems of unstable signal transmission,delayed information transmission caused by task concurrency,and unsustainable communication guarantee in emergency disaster rescue scenarios.At present,the application of UAV-assisted MEC still faces problems and challenges such as energy limitations,high transmission delays,and a lack of targeted system models.Therefore,it is of great research significance to establish a practical and reliable UAV-assisted MEC model from multiple aspects such as signal transmission and data calculation,and optimize system costs while considering energy and delay comprehensively.In this paper,aiming at the emergency communication scenario assisted by UAV,based on the transmission model verified by experiments,a UAV assisted MEC system model including calculation delay,Transmission delay,and energy consumption is constructed,and the problem is optimized based on heuristic algorithm and neural network algorithm to achieve the optimization of computing resources and energy efficiency in the UAV assisted MEC system.Firstly,a channel measurement platform based on Software Definition Radio(SDR)was established,and field measurements were conducted in an approximate open ground scenario.The Two Ray propagation model under open ground conditions was modeled and validated.Secondly,the calculation delay model and energy transfer model in the MEC system have been improved,and weight factors have been introduced based on the differences in transmission task types to allocate the proportion of delay and energy in the system model.Once again,the trajectory,communication resource allocation,and offloading ratio optimization of unmanned aerial vehicles were modeled as a joint optimization problem as a whole,and heuristic algorithms were used to gradually optimize the overall goal,achieving a 40% performance improvement.Furthermore,in response to the more intensive concurrent tasks and the trade-off between link load and transmission conditions in emergency scenarios,this article focuses on the requirements for task urgency in disaster relief scenarios and proposes an optimization strategy based on deep reinforcement learning to further reduce system latency and achieve online computing optimization of offloading ratio and resource allocation.Simulation results demonstrate the effectiveness of this algorithm in optimizing system latency,providing experimental verification and theoretical basis for the subsequent development and application of the UAV-assisted MEC system.