Method for assisting mobile unloading of users by multiple unmanned aerial vehicles

Abstract

The invention provides a method for assisting mobile unloading of users by multiple unmanned aerial vehicles, and belongs to the field of mobile cloud computing. The method comprises the steps of carrying out modeling on mobile migration of uploading and downloading requirements of multiple users from three aspects of unmanned aerial vehicle resource allocation, flight path planning and task allocation; and then converting and relaxing the model by a three-stage iterative algorithm, and performing iterative solution by using a branch-and-bound method to obtain an optical resource allocation, task allocation and flight path planning scheme. The mobile unloading method provided by the invention has the beneficial effects that the uploading demand and the downloading demand of the users are simultaneously taken into consideration, and the calculation rate of the users is greatly improved; and the mobile unloading method provided by the invention realizes the fairness of the users by maximizing the minimum calculation rate in all of the users.

Description

technical field [0001] The invention belongs to the field of mobile cloud computing, and in particular relates to a mobile unloading method in which multiple drones assist users. Background technique [0002] With the explosive growth of smartphones, tablet PCs, and desktop computers, mobile data traffic has grown exponentially, resulting in data traffic overload on the cellular network, which cannot provide enough capacity to meet many mobile data requirements. How to reduce the traffic carried on the cellular frequency band? Excessive data volume is a huge challenge now. Mobile offloading is an emerging technology to solve the above problems. It includes computing offloading and data offloading. It refers to the use of complementary network technologies to provide users with data traffic. Through offloading, the amount of data carried on the cellular frequency band is reduced, and bandwidth is released for other users. For some edge users, traditional base stations can no...

AI Technical Summary

Problems solved by technology

In the first category, a cellular-connected UAV mobile edge computing system is studied, where multiple UAVs are served by a ground base station (TBS) for computing task offloading, with the goal of minimizing the total energy consumption of UAVs, including propulsion energy , to calculate the energy and communication energy, while ensuring that the total number of bits of the UAV is fully calculated, but only four uplink transmissions are considered, and the user's data offloading requirements are not considered (Hua M, Huang Y, Wang Y, et al. Energy Optimization for Cellular-Connected Multi-UAV Mobile Edge Computing Systems with Multi-Access Schemes[J].2018.)

In the second category, a wireless communication system based on multi-UAV is studied, using multiple UAV-installed aerial base stations to serve a group of users on the ground, by optimizing multi-user communication scheduling and association, combined with UAV Trajectory and power control to maximize the minimum throughput of all ground users in the downlink communication, but did not consider the coexistence of task upload and data download from the user requirements (Wu Q, Zeng Y, Zhang R. Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks[J].IEEETransactions on Wireless Communications,2017,PP(99):1-1.)

[0005] (1) Many studies only consider the aspect of computing offloading, and do not consider the problem of downlink transmission from the aspect of user data download, ignoring the complex situation of user needs in actual scenarios

[0006] (2) Most of the research on multi-UAV as an air base station only considers the entire task for migration in task matching, and does not divide the matching relationship between UAVs and users according to time slots, which reduces the utilization efficiency of UAVs

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