PCICaching: Learning-Driven and Resilient UAV Caching with Cache-Aware User Association in SAGINs

Space–air–ground integrated networks (SAGINs) enable flexible content delivery through satellite–UAV–ground cooperation, yet time-varying user demand and dynamic backhaul conditions pose significant challenges to efficient UAV caching. To address these challenges, this paper proposes PCICaching, a backhaul-aware and prediction-driven UAV caching framework that integrates LSTM-based popularity forecasting, cache-aware user association, and conditionally activated cooperative caching. Under normal satellite backhaul conditions, PCICaching operates in a latency-oriented mode and reduces average content delivery latency by up to 33.9% and 38.9% compared with representative GTGA-based and history-based baselines, respectively. When backhaul connectivity degrades, the proposed C3 mechanism enlarges cluster-level content coverage and maintains service continuity with only a moderate latency increase of approximately 14.2%. Moreover, the proposed sequential decomposition enables scalable online operation with per-update execution time below 100 ms. These results demonstrate that PCICaching provides a structurally adaptive and computationally efficient solution for UAV-assisted caching in SAGINs, effectively balancing latency efficiency and content availability under time-varying demand and infrastructure uncertainty.