A Handover-Aware Congestion Control Algorithm Assisted by DRL in LEO Satellite Networks

Low earth orbit satellite networks (LEOSNs) are increasingly favored for providing ubiquitous Internet access. However, the dynamic characteristics in LEOSNs pose two challenging issues to congestion control algorithms (CCAs) in transport layer: 1) time-varying link capacity when links remain connected, leading to a continuous mismatch between CCA's sending rate and capacity, and 2) brief but significant link interruptions during satellite handovers. To address these issues, this paper proposes a two-phase CCA called Creo, in which each phase tackles one of the two aforementioned issues in LEOSNs individually. In Creo's connected phase where links remain connected, we design a deep reinforcement learning framework, which captures complex patterns of highly variable link capacities in LEOSNs to generate dynamically adaptive congestion control strategies. In Creo's handover phase where links suffer interruptions, we introduce a handover-aware process, which leverages cross-layer notifications to notify TCP sender in advance of handover occurrence to instruct sender when to stop and resume sending at precise timestamps. Extensive simulation results show that, compared to other CCAs, Creo consistently tracks time-varying capacity, reduces average handover recovery time by 60.5%, and overall achieves a 55% average throughput improvement while maintaining low latency and low delay jitter.