Unmanned aerial vehicle coverage optimization method based on signal to interference plus noise ratio probability perception

Abstract

The invention discloses an unmanned aerial vehicle coverage optimization method based on signal to interference plus noise ratio probability perception, and the method comprises the following steps: S1, carrying out signal to interference plus noise ratio (SINR) detection on an unmanned aerial vehicle and a user side, and constructing a probability perception model; S2, collecting a plurality of unmanned aerial vehicle abscissas as initial samples to be subjected to Logistic mapping processing, and obtaining an initialized population; S3, updating the position of the initialized population, calculating the fitness of the current individual, and performing sorting; S4, the average fitness of the current population is calculated, a random operator is used for disturbing the individuals falling into local optimum, and the obtained individuals with the optimal fitness are the unmanned aerial vehicle individuals with the optimal coverage rate; S5, obtaining an optimal coverage rate. According to the method, the quality of the initial solution can be improved by utilizing the Logistic chaotic sequence, and the global search capability of the algorithm is enhanced. In order to prevent individuals from falling into local optimum, two types of random operators are introduced, the local search capability is improved, the global optimal solution is improved, and the network coverage rate of the unmanned aerial vehicle is maximized.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicle network-assisted communication, in particular to an unmanned aerial vehicle coverage optimization method based on SINR probability perception. Background technique [0002] As a high-altitude communication platform, UAV (Unmanned Aerial Vehicle, UAV) has flexible mobility and good line-of-sight links, and is not affected by complex terrain conditions, so it has received extensive attention from the academic community in recent years. In the emergency communication scenario, when the base station on the ground is destroyed, the UAV self-organizing network can be deployed as an air base station to build a communication platform for ground users, especially to assist in the restoration of communication in post-disaster areas. However, due to the limited energy consumption of UAVs, rational deployment of the three-dimensional (3D) spatial location of UAVs to maximize the coverage of se...

AI Technical Summary

Problems solved by technology

In order to find the optimal solution to such problems, swarm intelligence algorithms are often used in the prior art. However, swarm intelligence algorithms generally have the problem that they are prone to fall into local optimum, and are prone to fall into the "premature" phenomenon, and the advantages and disadvantages of the initial population in the algorithm It will also affect the solution accuracy and convergence speed of the algorithm.

Images