空天地协同网络的边缘计算与资源分配OA

Low Earth orbit(LEO)satellite and high altitude platform(HAP)have become the key technologies to achieve all-domain,full-time,and full-coverage communication.In order to better provide efficient and stable services for ground users,aiming at the HAP-assisted LEO satellite edge computing,a three-layer network architecture composed of terminals,HAPs,and LEO satellites is proposed,and the tasks can be processed on the three platforms,and the satellites can also cooperate to achieve on-board load balancing.Considering the problems of time delay,resource constraints,the high complexity of the modeling problem and the rapid fading of satellite-to-ground channels,by jointly optimizing offload decisions,bandwidth and computing resource allocation strategies,a task offloading and resource allocation algorithm based on deep deterministic policy gradient is proposed,which models the problem as a Markov decision process,and preprocesses the environmental state parameters by state normalization algorithm.Compared with three strategy algorithms including deep Q network,full offloading and no inter-satellite link,the proposed algorithm shows excellent performance in terms of delay and energy consumption.