FTRL-WRR: Learning-Based Two-Path Scheduler for LEO Networks

Two-path transmission with the involvement of LEO satellites is an increasingly common scenario. LEO networks may offer higher bandwidth compared with some terrestrial networks, such as cellular, but often come with increased variability in latency. Effective traffic allocation to maximize bandwidth utilization in such dynamic environments is challenging. This paper addresses two-path scheduling problem under a high dynamic environment by proposing the FTRL-WRR algorithm, which combines a lightweight, learning-based optimization algorithm with a weighted round-robin scheduler. We model traffic allocation as a 1-dimensional optimization problem and demonstrate the algorithm's effectiveness through evaluations in emulated Starlink-cellular scenarios. Results show that FTRL-WRR improves bandwidth utilization and reduces median completion time by up to 27.71%.