Dynamic task offloading method based on non-orthogonal multiple access and UAV assistance

By introducing a dynamic task offload method based on non-orthogonal multiple access in the drone mobile edge computing model, dynamically adjusting the flight trajectory and power control strategy of the drone, the problem of poor computing efficiency and resource allocation effects in complex dynamic environments is solved, and more efficient resource utilization and system performance improvement is achieved.

CN119212001BActive Publication Date: 2025-03-18CHANGCHUN UNIV OF SCI & TECH
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Patent Information

Application Number
CN202411266601.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-09-11
Publication Date
2025-03-18
Estimated Expiration
2044-09-11

AI Technical Summary

Technical Problem

The existing drone mobile edge computing model is too single, and does not consider the movement of drones and ground users in real environments, resulting in a greatly reduced system computing efficiency and resource allocation effect in complex dynamic environments.

Method used

Using a dynamic mission offloading method based on non-orthogonal multiple access and drone assistance, by building a drone-assisted edge computing framework and mathematical model combining non-orthogonal multiple access, dynamically adjusting the flight trajectory and power control strategy of the drone, optimizing the offloading strategy and the selection of drones by intelligent mobile devices to improve system performance.

Benefits of technology

It significantly improves the overall performance of the system in multi-user and multi-task scenarios, improves computing resource utilization and dynamic adaptability, and enhances the decision-making accuracy of task offloading and the system coverage capability.

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Abstract

The present invention belongs to the technical field of resource optimization and allocation, and is particularly a dynamic task unloading method based on non-orthogonal multiple access and unmanned aerial vehicle assistance. The method specifically includes the following steps: S1, constructing a unmanned aerial vehicle assisted edge computing framework combined with non-orthogonal multiple access, the framework including an unmanned aerial vehicle base station, a ground base station and an intelligent mobile device. In the edge computing framework, the intelligent mobile device unloads the tasks to be executed on the local side and the unmanned aerial vehicle side respectively, and each unmanned aerial vehicle dynamically adjusts the trajectory according to the mobile position of the device, and establishes a mathematical model of unmanned aerial vehicle assisted edge computing combined with non-orthogonal multiple access; S2, based on the unmanned aerial vehicle assisted edge computing framework and mathematical model of non-orthogonal multiple access, construct a dynamic task unloading problem. The present invention can be applied in the Internet of Things system, effectively alleviating the problem of tight computing resources in intensive task scenarios, and making up for the fact that traditional ground cellular networks are difficult to meet users' needs for real-time response and wide-area coverage.
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