The invention claims to protect an unmanned aerial vehicle air-ground
urban environment channel modeling and prediction method based on measured data, and aims to solve the problems that unmanned aerial vehicle air-ground channel modeling is disjointed with an actual scene, the
path loss prediction precision is low, and integrated
system support is lacked. And efficient and accurate modeling and prediction of the air-ground channel of the unmanned aerial vehicle in an
urban environment are realized. The method comprises the following steps: step 1), constructing an air-ground channel measurement
system, carrying out
urban environment multi-band actual measurement, and constructing a
channel data set; step 2), carrying out statistical channel modeling based on measured data, and analyzing large-scale and small-scale
fading characteristics; step 3), designing a
path loss prediction model PBANET based on
deep learning; the unmanned aerial vehicle air-to-ground channel modeling and predicting method can be applied to
communication system design and optimization of scenes such as unmanned aerial vehicle disaster rescue and emergency communication, compared with an unmanned aerial vehicle air-to-ground channel modeling method of CN120090745B, the unmanned aerial vehicle air-to-ground channel modeling and predicting method is more suitable for urban air-to-ground communication scenes, and modeling precision and
engineering practicability are remarkably improved.