  Low earth orbit (LEO) satellite-assisted communications have been considered
as one of key elements in beyond 5G systems to provide wide coverage and
cost-efficient data services. Such dynamic space-terrestrial topologies impose
exponential increase in the degrees of freedom in network management. In this
paper, we address two practical issues for an over-loaded LEO-terrestrial
system. The first challenge is how to efficiently schedule resources to serve
the massive number of connected users, such that more data and users can be
delivered/served. The second challenge is how to make the algorithmic solution
more resilient in adapting to dynamic wireless environments.To address them, we
first propose an iterative suboptimal algorithm to provide an offline
benchmark. To adapt to unforeseen variations, we propose an enhanced
meta-critic learning algorithm (EMCL), where a hybrid neural network for
parameterization and the Wolpertinger policy for action mapping are designed in
EMCL. The results demonstrate EMCL's effectiveness and fast-response
capabilities in over-loaded systems and in adapting to dynamic environments
compare to previous actor-critic and meta-learning methods.
