Multiple network interfaces equipped with terminal nodes in heterogeneous wireless networks can be used for multi-path transmission, transmitting data in parallel to improve the throughput of network communication, as well as the stability and resilience of the connection. However, existing multipath congestion control algorithms have difficulty in dealing dynamic changing network environments and ignore the subflow coupling feature, resulting in problems such as underutilization of multipath resources and transmission fairness. To address these challenges, we propose a novel multi-path intelligent congestion control algorithm based on subflow coupling perception (MSCP), which utilizes deep reinforcement learning (DRL) techniques to improve the adaptability to dynamic network environments. The subflow coupling features are perceived through the analysis of trends in subflow transmission round-trip time. To further refine this understanding, Long Short-Term Memory (LSTM) is utilized to eliminate network noise and extract the latent temporal information inherent in subflow states. By providing a more precise estimation of network conditions, this approach enhances the ability of DRL agents to learn more effectively from their interactions with the environment. Proximal Policy Optimization (PPO) algorithm is combined with Coupled Bottleneck Bandwidth and Round-trip propagation time (Coupled BBR) multipath congestion control algorithm, where the transmission gain coefficients of each subflow is adaptively adjusted using PPO agents to control the transmission rate of each subflows, improving the algorithm’s responsiveness to dynamic network environments. Experimental results show that the proposed algorithm effectively utilizes link resources, leading to significant improvements in both transmission throughput and transmission fairness.
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