UAV Edge Caching Content Recommendation Algorithm Based on Graph Neural Network

Abstract

When responding to emergencies such as sudden natural disasters, communication networks face challenges such as network traffic surge and complex geographic environments. Aiming at the problems of high transmission delay and insensitivity to user's preference in the current UAV edge caching strategy, this paper proposes a UAV caching content recommendation algorithm based on graph neural network. Firstly, the location of UAV is determined by clustering algorithm; secondly, the interest preferences of user nodes in the cluster are predicted by GCLRSAN model, and the UAV cache content is designed according to the result; finally, simulation experiments show that the model and algorithm proposed in this paper can effectively reduce the backhaul link overhead and outperform the comparison algorithms in the indexes such as accuracy rate, recall rate, cache hit rate, and transmission delay.