  Constructing earth-fixed cells with low-earth orbit (LEO) satellites in
non-terrestrial networks (NTNs) has been the most promising paradigm to enable
global coverage. The limited computing capabilities on LEO satellites however
render tackling resource optimization within a short duration a critical
challenge. Although the sufficient computing capabilities of the ground
infrastructures can be utilized to assist the LEO satellite, different
time-scale control cycles and coupling decisions between the space- and
ground-segments still obstruct the joint optimization design for computing
agents at different segments. To address the above challenges, in this paper, a
multi-time-scale deep reinforcement learning (DRL) scheme is developed for
achieving the radio resource optimization in NTNs, in which the LEO satellite
and user equipment (UE) collaborate with each other to perform individual
decision-making tasks with different control cycles. Specifically, the UE
updates its policy toward improving value functions of both the satellite and
UE, while the LEO satellite only performs finite-step rollout for
decision-makings based on the reference decision trajectory provided by the UE.
Most importantly, rigorous analysis to guarantee the performance convergence of
the proposed scheme is provided. Comprehensive simulations are conducted to
justify the effectiveness of the proposed scheme in balancing the transmission
performance and computational complexity.
