Dynamic Space-Ground Integrated Mobility Management Strategy for Mega LEO Satellite Constellations

To deal with the challenges in mobility management of mega low-Earth-orbit (LEO) satellite constellations with long management delays and high signaling overheads, especially under the existing fixed and limited deployments of ground mobility management entities, the cooperative mobility management mode of medium-Earth-orbit (MEO) satellites and ground stations (GSs) has become an attractive tendency. In this paper, considering with the global non-uniform user distribution and constrained satellite storage resources, we propose a dynamic satellite-ground integrated mobility management strategy (DSG-MMS) to cope with the relative mobility among users, GSs, and satellites, which can dynamically decide the optimal GS/MEO management node with the minimal handover and migration delays. Specifically, the DSG-MMS optimization problem is modeled as distributed Markov decision processes, and a reinforcement learning (RL)-based management node selection method is presented to solve them, where each LEO satellite agent dynamically decides its own management node. To further implement the RL algorithm on the resource-limited LEO satellite agents, a novel tensor-based RL algorithm for DSG-MMS is proposed by means of the streamed low-rank tensor decomposition, where only the small-sized core tensor and factor matrices are kept and updated in the strategic optimization so as to realize low storage and computing overheads as well as fast convergence. We perform simulations for the proposed DSG-MMS with parameter configurations of actual Telesat, Kuiper, and Starlink satellite systems to evaluate the mobility management delay and overhead performances as well as the required satellite storage size for mobility management. Moreover, a case study and an architectural comparison are given to demonstrate the superiority of the proposed DSG-MMS than existing methods.