Collaborative Optimization Model of Highway Network with Tourism Resources as the Target Node

Abstract

In order to improve the collaborative optimization effect of tourism resources and highway network, this paper combines deep learning algorithm to construct a collaborative optimization model of tourism resources and highway network, and adopts an algorithm based on continuous convex approximation. By iterating the optimal solution obtained each time, a high-quality approximate beamforming matrix and artificial noise covariance matrix can finally be obtained, which eliminates the problem that the traditional algorithm cannot solve the noise. Moreover, this paper introduces artificial noise to prove that the rank relaxation is compact by considering the corresponding minimization power problem. The simulation results show that the proposed scheme and approximation algorithm can obtain better system security and speed than the existing literature, and there are certain improvements compared with the traditional method, so the effectiveness of the method in this paper is verified by simulation experiments.