Machine Learning-Driven Content Popularity Prediction and Cache Optimization in D2D Clustered Networks

Abstract

Advancements in wireless communication technology have led to the widespread use of smart devices including computers, mobile phones, tablets, wearable devices, and vehicles which has significantly increased the demand for high quality content. This growing demand puts pressure on the backhaul links in cellular networks, resulting in congestion and content delivery delays. To address this, cache enabled networks and edge caching, such as caching in user devices, have emerged as promising solutions to reduce backhaul traffic. By caching content locally and using device to device (D2D) communication for retrieval, content delivery can be made more efficient. However, limited cache capacity requires intelligent content selection strategies. The popularity of the content is dynamic and varies with user preferences, where less than 20% of the users generate 80% of multimedia traffic. Many existing methods fail to consider this user heterogeneity, often assuming uniform preferences throughout the network. This paper proposes a novel Machine Learning Driven Content Popularity Prediction and Cache Optimization (ML CPCO) framework that dynamically predicts user and cluster level content demand, incorporates user willingness to participate in caching, and optimizes cache placement in D2D enabled clustered networks. The system predicts future content requests using machine learning algorithms and estimates content popularity at the cluster level. Based on these predictions, cache placement decisions are made to maximize efficiency. The simulation results show that the proposed approach performs well under various network conditions, achieving a cache utilization rate of nearly 97% the highest among the methods compared. In addition, it offers an improved hit rate with an acceptable execution time, resulting in reduced backhaul traffic and enhanced user experience.