Scheduling and Optimization of Wireless Virtual Network Resources Based on Reinforcement Learning

Abstract

With the booming and proliferation of 5G wireless network services in the future, a large number of wireless virtual network resources will emerge, and the densification and heterogeneity of the wireless communication networks that provide services for them will become the trend of development. To this end, a wireless virtual network resource scheduling model based on user satisfaction is constructed, and a reinforcement learning-based wireless virtual network resource scheduling mechanism, IRSUP, is proposed. IRSUP is designed with an intelligent optimization module for user service preferences to address the personalized needs of user service customization, and a reinforcement learning-based intelligent scheduling module is designed to address the challenge of joint optimization of multistar resources. Simulation results show that IRSUP can effectively improve resource scheduling rationality and link resource utilization and user satisfaction, among which service capacity is improved by 30% to 60% and user satisfaction is more than doubled.