Researches On Resource Optimization Technology Of UAV-Enabled Mobile-Edge Computing Networks

Mobile-edge computing and wireless power transfer are two key techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things,However,the computation performance and the harvested energy are significantly impacted by the severe propagation loss.In order to solve the problem of limited computing delay and energy storage in edge computing networks.This paper studies the resource optimization strategy for maximizing computation bits of UAV-enabled mobile edge computing network,which improves the computing rate and energy harvesting efficiency.Under the partial computation offloading mode,the optimal resource allocation strategy for maximizing computation efficiency is designed to improved computational energy efficiency.The specific contributions are summarized as follows:In the UAV-enabled wireless powered mobile-edge computing network,a resource allocation optimization framework for maximizing the computation bits problem is established.Using convex theory,the optimal CPU frequency,user's offloading time,transmit power,and UAV trajectory strategy are jointly optimized.In the partial computation offloading mode,a two-stage alternative algorithm is proposed,and closed-form expressions are given for the optimal CPU frequency,user's offloading time,and transmit power.Simulation results show that the proposed resource allocation schemes outperform other benchmark schemes.The results also demonstrate that the proposed schemes converge fast and have low computational complexity.The resource allocation method that maximizes the computation bits or minimizes the computation energy consumption focuses on a single optimized performance index,and an effective trade-off between computation bits and computation efficiency cannot be achieved.Based on the above work,this study establishes a resource allocation problem that optimizes computation efficiency.Under the partial computation offloading mode,the offloading time,CPU frequency,the transmit power,and UAV trajectory are jointly optimized.For the problem of non-convex computation efficiency,a two-stage iterative algorithm is proposed,and closed-form expressions for CPU frequency and the transmit power are given.Simulation results show that there is a tradeoff between computation rate and computation efficiency.It also shows that the joint optimization scheme has higher computation efficiency than other benchmark schemes.