2026/08/03 by Víctor Vilchez, Tiago P. C. de Andrade, Edward Hinojosa +2
Computer Science · #cs.NI
This paper was accepted for publication at the IEEE Global Communications Conference (GLOBECOM 2026)
arxiv created 2026/08/03 · arxiv updated 2026/08/04
The integration of Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs) into 5G and upcoming 6G architectures introduces various challenges, including severe propagation delays, ultra-high base station mobility, and channel non-stationarity, complicating radio resource management of heterogeneous network slices. In this paper, we propose a deep reinforcement learning (DRL) meta-scheduler for twin-timescale resource allocation. Our solution adopts a decoupled Open Radio Access Network (RAN) architecture, in which a strategic 100 ms meta-scheduler selects scheduling policies for the different network slices using stale telemetry, while a fast-timescale MAC packet scheduler processes per-TTI user requests. The resulting Markov Decision Process captures non-stationary orbital dynamics and heterogeneous SLAs constraints via a TD3 agent. Simulation results under varying traffic load show that, unlike other solutions, the proposed meta-scheduler explicitly trades a statistically insignificant 1% capacity fraction (p > 0.05) to strictly bound the variance and overall magnitude of RLC-layer queuing delay for Mission-Critical (MC) traffic. Crucially, it enforces this isolation without inducing the broadband slice starvation characteristic of standard maximum-CQI heuristics, establishing a robust foundation for 6G O-RAN NTN resource allocation.