Unmanned aerial vehicle spectrum resource allocation method based on block coordinate descent

Abstract

The invention provides an unmanned aerial vehicle spectrum resource allocation method based on block coordinate descent. The method comprises the following steps of determining the number of unmannedaerial vehicles, setting a system licensed channel set and setting the system maximum power upper limit and other environment parameters; establishing a spectrum allocation matrix and an adjacent channel interference matrix, importing an original problem, and simplifying an original complex max-min mixed planning problem through utilization of problem structure features; providing a low complexityiterative algorithm based on the block coordinate descent, thereby solving the problem; and carrying out power and channel allocation on the unmanned aerial vehicles by a ground control station according to the maximum SINR value of each unmanned aerial vehicle through utilization of remote control channels, thereby finishing designing the unmanned aerial vehicle time-frequency resource allocation method. The method has the beneficial effects that according to the unmanned aerial vehicle spectrum resource allocation method based on the block coordinate descent, on the premise of improving thereliability of receiving control signals by each unmanned aerial vehicle and ensuring the system maximum consumption power constraint, limited spectrum resources are reasonably allocated.

Description

Technical field [0001] The present invention belongs to the field of UAV communication technology, and specifically is a UAV spectrum resource allocation method based on block coordinate drop. Background technique [0002] With the improvement of sensing technology and computing power, drones have gradually become a popular choice in applications such as urban search, military reconnaissance, and agricultural supervision. However, due to challenges such as exponential growth in demand for UAV wireless services and tight spectrum resources, UAV communication is facing severe challenges. At the same time, the collaborative operation of multiple drones also brings many challenges to be considered. Among them, a key technical challenge is that the safety and stability of UAV communication requires the control signal itself to have a certain degree of robustness, and the quality of the control signal is very sensitive to channel changes and interference; another challenge is, The av...

AI Technical Summary

Problems solved by technology

However, due to challenges such as the exponential growth of UAV wireless service demand and the shortage of spectrum resources, UAV communication is facing a severe test

At the same time, the collaborative operation of multiple UAVs also brings many challenges that need to be considered

Among them, a key technical challenge is that the security and stability of UAV communication requires a certain robustness of the control signal itself, and the quality of the control signal is very sensitive to channel changes and interference; another challenge is, The resources available for communication are often finite, exacerbating potential mutual interference effects

[0003] However, none of the existing work has studied how to improve the utilization of UAV spectrum resources and the allocation of limited time-frequency resources for multi-UAV systems.

In fact, the UAV's control signal reception is not only affected by the link quality of the communication channel, but also easily affected by potential interference

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