GRU-QUIC: A Machine Learning-Enhanced QUIC Protocol with Packet Reordering Resilience for Satellite Networks

Satellite communication environments, characterized by dynamic links, exacerbate packet reordering, which leads to fundamental limitations in QUIC’s loss detection mechanism. A fixed packet threshold may misinterpret reordered packets as lost, leading to spurious retransmissions and throughput degradation. To address the challenge, we propose GRU-QUIC, a machine learning-enhanced QUIC protocol with packet reordering resilience for satellite networks. First, we model the relationship between the probability of spurious packet loss due to reordering and the throughput of QUIC, providing a theoretical basis for performance optimization. Second, we design a gated recurrent unit (GRU)-based prediction module on the receiver side to predict the next slot packet reordering displacement by learning the temporal correlation of packet reordering sequences. The sender then uses the reordering displacement indicated by the GRU to dynamically adjust the packet threshold of loss detection, effectively alleviating the problem of packet loss misjudgment due to packet reordering. Experiments demonstrate that GRU-QUIC significantly enhances the performance of QUIC transmission while maintaining protocol compatibility. In a simulated satellite network, GRU-QUIC effectively reduces unnecessary retransmissions and shortens file download completion time compared to standard QUIC.