Research On Trade-off Of Air-ground Energy Consumption Of UAV-assisted Mobile Edge Computing

The rapid development of Internet of Things technology promotes the emergence of many emerging applications,such as intelligent grazing,environmental monitoring,autonomous driving and interactive games,etc.However,mobile user terminals usually cannot meet the low-latency requirements of applications in communication and computing due to limited resources..Mobile Edge Computing(MEC)technology has become an effective solution by providing dense computing services with lower transmission delay at access points located at the network edge,reducing the amount of offloading to remote clouds.However,traditional MEC is inaccessible in some complex terrains or bad weather,and dense deployment of MEC servers is expensive and impractical.Unmanned Aerial Vehicle(UAV)is considered to be an important means to install MEC server to provide computing support for mobile users due to its outstanding low cost,maneuverability and hovering ability,and is widely used by academia and industry.focus on.The UAV-assisted MEC system can achieve cellular network coverage in remote areas with insufficient ground infrastructure or emergency situations such as disaster relief,and provide computing services to ground users by utilizing good line-of-sight links.Secondly,the size and battery capacity of drones and ground users are limited,and frequent charging or battery replacement will bring huge labor and environmental costs.The amount of computing tasks the drone can complete.Theoretically,the UAV can obtain a better communication channel by being close to the ground user.At this time,the user will generate less communication energy consumption.However,this flight trajectory will cause the UAV to have greater propulsion energy consumption.Therefore,the reduction of ground user energy consumption usually comes at the expense of increasing the energy consumption of UAVs,which leads to a new fundamental trade-off relation of energy consumption in UAV-assisted MEC communication systems.Existing researches on energy consumption optimization of UAV-based MEC mainly aim to minimize UAV energy consumption,user energy consumption or total system energy consumption.The problem of energy consumption,such a design is inefficient from the perspective of the overall energy consumption optimization of the communication system.In addition,the energy consumption of UAVs and ground users is usually not at the same level.How to describe the trade-off relationship between air and ground energy consumption under the premise of ensuring fairness,in order to improve the computing performance of the communication system and reduce energy consumption to extend the system important in terms of life cycle.Based on the above background,this paper mainly considers two scenarios of UAV-assisted ground-to-ground communication,combining two technologies of cooperative communication and MEC to improve the computing performance of the system.The design and optimization of the trajectory are carried out,and the MEC calculation offloading strategy to reduce the total energy consumption of the system in different communication scenarios is proposed,and the trade-off relationship between air and ground energy consumption is further clarified.The main work is as follows:(1)Energy consumption tradeoffs in drone-assisted MEC.This scenario considers a rotary-wing UAV equipped with an MEC server to provide computing offloading services for multiple ground users who generate computing-intensive and delay-sensitive tasks.As the energy consumption of mobile users and drones are not at the same level,an amplification factor is introduced to balance the energy consumption of the two.We propose a joint optimization algorithm for the transmit power of ground users,computational task assignment and UAV trajectory to minimize the total energy consumption of the communication system.In order to solve the highly non-convex problem,a two-stage resource allocation strategy based on alternating optimization is proposed to obtain the optimal solution.Numerical simulation plots show that the algorithm has a significant performance advantage in reducing total energy consumption in all benchmark schemes.(2)Research on weighted energy consumption optimization of UAV-based MEC communication system in backhaul network.When the amount of computing tasks for ground users is large,relying only on the MEC server carried by the UAV for auxiliary computing will have the risk of not being able to complete the computing task.Therefore,consider using the UAV as a relay platform and MEC server when the base station is introduced.To meet the larger mission needs of ground users.In this study,the multi-objective optimization framework of the UAV-to-ground communication system with the introduction of MEC technology into the wireless backhaul network is first established,in which the UAV and the base station are equipped with MEC servers.Computing tasks are offloaded to UAVs and base stations for auxiliary computing.Under the constraints of backhaul rate and ground user computing tasks,the unloading time,computing task allocation,ground user launch power and UAV trajectory are jointly optimized to minimize the total system energy consumption.In addition,by introducing an amplification factor and a trade-off factor,fairness is taken into account while considering the trade-off relationship between UAV energy consumption and user energy consumption.Since this optimization problem is difficult to solve directly,we propose a two-stage alternating iterative optimization algorithm based on sub-gradient algorithm and continuous convex approximation technique.The simulation results show that the two-stage algorithm can obtain better optimization results.