The proliferation of artificial intelligence applications on edge devices necessitates efficient transport protocols that leverage multi-homed connectivity across heterogeneous networks. While Multipath TCP enables bandwidth aggregation, its in-kernel congestion control mechanisms lack the programmability and flexibility needed for achieving efficient transmission. Additionally, inherent measurement noise renders network state partially observable, challenging data-driven approaches like deep reinforcement learning (DRL). To address these challenges, we propose a Transformer-based Congestion Control Optimization (TCCO) framework for multipath transport. TCCO employs a decoupled architecture that offloads control decisions to an external decision engine via a lightweight in-kernel client and user-space proxy, enabling edge devices to leverage external computational resources while maintaining TCP/IP compatibility. The Transformer-based DRL agent in the external decision engine uses self-attention to capture temporal dependencies, filter noise, and coordinate control across subflows through a unified policy. Extensive evaluation on both simulated and real dual-band Wi-Fi testbeds demonstrates that TCCO achieves superior adaptability and performance than state-of-the-art baselines, validating the feasibility and effectiveness of TCCO for wireless networks.

翻译：边缘设备上人工智能应用的激增，要求传输协议能够高效利用异构网络的多宿主连接能力。尽管多路径TCP（Multipath TCP）能够实现带宽聚合，但其内核拥塞控制机制缺乏实现高效传输所需的可编程性与灵活性。此外，固有的测量噪声使得网络状态仅部分可观测，这对深度强化学习（DRL）等数据驱动方法构成了挑战。为应对这些挑战，我们提出了一种基于Transformer的多路径传输拥塞控制优化框架TCCO。TCCO采用解耦架构，通过轻量级内核客户端和用户空间代理将控制决策卸载至外部决策引擎，使边缘设备能够在保持TCP/IP兼容性的同时利用外部计算资源。外部决策引擎中基于Transformer的DRL智能体利用自注意力机制捕捉时序依赖、过滤噪声，并通过统一策略协调各子流的控制。在仿真和真实双频Wi-Fi测试床上的大量评估表明，TCCO相比现有先进基线方法具有更优的适应性与性能，验证了TCCO在无线网络中应用的可行性与有效性。