A knowledge graph-based reinforcement learning approach for cooperative caching in MEC-enabled heterogeneous networks

Existing wireless networks are flooded with video data transmissions, and the demand for high-speed and low-latency video services continues to surge. This has brought with it challenges to networks in the form of congestion as well as the need for more resources and more dedicated caching schemes. Recently, Multi-access Edge Computing (MEC)-enabled heterogeneous networks, which leverage edge caches for proximity delivery, have emerged as a promising solution to all of these problems. Designing an effective edge caching scheme is critical to its success, however, in the face of limited resources. We propose a novel Knowledge Graph (KG)-based Dueling Deep Q-Network (KG-DDQN) for cooperative caching in MEC-enabled heterogeneous networks. The KG-DDQN scheme leverages a KG to uncover video relations, providing valuable insights into user preferences for the caching scheme. Specifically, the KG guides the selection of related videos as caching candidates (i.e., actions in the DDQN), thus providing a rich reference for implementing a personalized caching scheme while also improving the decision efficiency of the DDQN. Extensive simulation results validate the convergence effectiveness of the KG-DDQN, and it also outperforms baselines regarding cache hit rate and service delay.