Information-Aware Driven Dynamic LEO-RAN Slicing Algorithm Joint With Communication, Computing, and Caching

With the rapid development of applications with different use cases and service demands for edge network, network slicing is an emerging solution for satisfying service-oriented requirements, while the low earth orbit (LEO) satellite caching-assisted communication has been considered as one of the key elements for effective services. With limited resources at the edge of the radio access network (RAN), it is challenging to take advantage of the LEO content cache to joint allocation of communication, computing and caching space (3C) resources. To this end, we investigate the problem of resource slicing and scheduling of joint 3C resources in RAN edge scenario assisted by LEO content caching. A hierarchical resource slicing framework is proposed for dynamic allocation of multidimensional resources. The optimization variables are relaxed and the constraints are adjusted. The sequential quadratic programming (SQP) iteration algorithm is proposed as theoretical offline baseline. Due to its complex solving process and limited real-time performance, we incorporate Long Short-Term Memory (LSTM) into the Soft Actor-Critic (SAC) algorithm to aware extract the distribution characteristics of historical information and propose the deep reinforcement learning algorithm of LSTM-SAC. Meanwhile, the proportional priority based scheduling algorithm is employed in the intra-slice. Compared to SAC, TD3 and DDPG algorithms, the proposed algorithm is the closest to the theoretical value, improves the objective function by 6.95%, 9.52% and 11.52% respectively, which can significantly improve the system rate while satisfying the service level agreements.