Deep Reinforcement Learning-based Adaptive Clustering Approach in Short Video Sharing through D2D Communication
Wentao Dong, Zhuo Li, Xin Chen · 2021
With the rapid popularity of short video applications, short videos sharing based on D2D multicast communication is considered as a promising technology to improve the quality of service. In this paper, we propose an adaptive clustering strategy in a short videos distribution scenario to group short video users into clusters. Specifically, a cluster heads (CHs) control algorithm based on deep reinforcement learning is proposed to determine the number of CHs adaptively for different system states. After that, we propose the bisecting K-means based on the physical-interest distance algorithm (PIBK-means) and select the CH of each cluster by analyzing the distance-power weight. Simulation results verify the effectiveness of the proposed adaptive clustering strategy and show that the proposed strategy can increase the degree of satisfaction by an average of 40% than the K-means algorithm.