Handover-Aware Cache Replacement Strategy in Non-Terrestrial Network: A Deep Reinforcement Learning Approach
Chuxing Fang, Han Xiao, Zitong Li, Zhenhui Yuan, Changqiao Xu · 2024
Non-terrestrial networks are regarded as crucial infrastructure in forthcoming 6G networks. Edge caching services can be deployed on satellites to optimize the transmission delay of non-terrestrial networks. The dynamically changing user requests and the limited cache space challenge the decision-making process for satellite caching. This paper proposes a Handover-Aware Cache Replacement (HACR) strategy to dynamically replace cache content with satellite mobility. The strategy incorporates the deterministic mobility of satellites to make optimal caching decisions over time and utilizes deep reinforcement learning to implement the strategy. We develop a simulation platform based on the configuration of the Starlink constellation and compare our proposed strategy with conventional methods. The results demonstrate that HACR achieves a 16.74% improvement in cache hit rate and a 48.9 % improvement in stability of cache hit rate compared to the state-of-the-art cache strategy.