Deep Reinforcement Learning-Based Content Caching in Satellite -Terrestrial Assisted Airborne Communications

With the continuous development of airborne communication, the demand for efficient internet access on airplanes has been increasing. To enhance the communication service quality for airborne users and address the challenge of high content request latency, a three-layer communication structure with satellite and terrestrial-assisted caching is proposed. In this structure, satellites, base stations, and aircraft cooperatively cache content to serve users aboard airplanes. Considering variations in request preferences, content popularity in aircraft, base stations, and satellites, as well as constraints related to cache space and communication duration, a content placement problem is formulated to minimize the total system latency. To tackle this problem, the content placement and delivery process is modeled as a Markov decision process (MDP). Subsequently, a Deep Reinforcement Learning (DRL)-based airborne communication cache placement algorithm named ACCP is introduced to derive optimal content placement decisions. Additionally, we expedite the convergence of ACCP with a prioritized experience replay mechanism and reduce time complexity using a sumTree data structure. Simulation results demonstrate that the proposed method significantly improves cache hit rate and reduces content delivery latency compared to other schemes.