Multi Wireless Access Point Collaborative Intelligent Access Control Mechanism Based on DRL

Due to a large number of users access, low rate users will aggravate the air port resource tension, reduce the capacity of wireless access points, resulting in the deterioration of user experience. In order to improve the overall performance of the network, the deep reinforcement learning builds an intelligent access control model on the basis of multiple wireless access point collaboration. Meanwhile, adaptive learning and optimization in dynamic environment to realize efficient utilization of network resources. The results show that the proposed method performs well in video playback applications in three indicators: average throughput, user experience quality and fairness. Under the proposed method, the user experience quality is basically maintained at 0.78. In application scenarios such as video playback that require high user experience quality, this method can provide better user experience and meet users' needs for high-quality network services. The proposed method shows significant advantages in terms of average throughput, user experience quality and resource allocation equity. This study provides an efficient solution for future wireless communication networks to promote the development of the field of intelligent control.