Interest-Aware Cooperative Caching for Symmetric Space–Air–Ground Integrated Networks

The space–air–ground integrated network (SAGIN) is a key 6G architecture that provides seamless three-dimensional connectivity, exhibiting hierarchical structural symmetry between LEO satellite and HAP layers. Integrating information-centric networking (ICN) with caching on Low Earth Orbit (LEO) satellites and high-altitude platforms (HAPs) significantly enhances content distribution efficiency. Existing studies on caching mechanisms have made progress but lack optimized cache resource allocation and accurate popular content identification. Thus, an interest-aware caching scheme (ICRL) based on reinforcement learning is proposed to optimize the SAGIN’s popular content caching decisions, aiming to achieve rational symmetric allocation of cache resources across LEO and HAP layers. Different from existing RL-based caching methods, the proposed ICRL scheme considers the LEO-HAP hierarchical architecture and designs an improved reinforcement learning mechanism to adapt to the dynamic characteristics of the SAGIN. First, an air–space two-tier caching architecture is constructed to enable collaborative caching between LEO satellites and HAPs. Second, to select high-value nodes intelligently, the proposed scheme leverages a comprehensive importance model that quantitatively analyzes HAP and LEO indicators such as topology, transmission capacity, and location. Finally, a reinforcement learning-based dynamic cache mechanism is developed. It captures real-time network requests and cache states to select optimal actions and adapt to network dynamics for better content popularity matching. Extensive evaluations based on NDNSIM demonstrate that ICRL outperforms baseline schemes in terms of cache hit ratio, server load, and request latency and achieves a symmetric balance of network load and service performance in the whole SAGIN.