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Low earth orbit (LEO) satellite-assisted communications have been consideredas one of key elements in beyond 5G systems to provide wide coverage andcost-efficient data services. Such dynamic space-terrestrial topologies imposeexponential increase in the degrees of freedom in network management. In thispaper, we address two practical issues for an over-loaded LEO-terrestrialsystem. The first challenge is how to efficiently schedule resources to servethe massive number of connected users, such that more data and users can bedelivered/served. The second challenge is how to make the algorithmic solutionmore resilient in adapting to dynamic wireless environments.To address them, wefirst propose an iterative suboptimal algorithm to provide an offlinebenchmark. To adapt to unforeseen variations, we propose an enhancedmeta-critic learning algorithm (EMCL), where a hybrid neural network forparameterization and the Wolpertinger policy for action mapping are designed inEMCL. The results demonstrate EMCL's effectiveness and fast-responsecapabilities in over-loaded systems and in adapting to dynamic environmentscompare to previous actor-critic and meta-learning methods.

Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling