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arXiv 2603.04550cs.NI
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基于Transformer的多路径拥塞控制:一种用于无线上行链路的解耦方法

Transformer-Based Multipath Congestion Control: A Decoupled Approach for Wireless Uplinks

The University of Hong Kong(香港大学);Beijing Jiaotong University(北京交通大学);City University of Hong Kong(香港城市大学);Waseda University(早稻田大学);University of Hong Kong(香港大学);Southwest University(西南大学);Xiamen University of Technology(厦门理工学院);Fudan University(复旦大学);Nanyang Technological University(南洋理工大学)

Zongyuan Zhang, Tianyang Duan, Liang Wang, Zihan Fang, Zheng Lin, Yijun Lu, Jiening Wu, Xia Du, Miao Yang, Zhe Chen, Heming Cui, Jun Luo

AI总结:

本文提出基于Transformer的多路径拥塞控制优化框架,通过解耦架构和深度强化学习实现高效无线上行链路传输。

摘要:

人工智能在边缘设备上的普及需要高效的传输协议,这些协议能够利用异构网络中的多宿连接。虽然多路径TCP能够实现带宽聚合,但其内核拥塞控制机制缺乏必要的可编程性和灵活性,无法实现高效传输。此外,固有的测量噪声使网络状态部分可观测,给数据驱动方法如深度强化学习(DRL)带来了挑战。为了解决这些挑战,我们提出了一种基于Transformer的拥塞控制优化(TCCO)框架用于多路径传输。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到外部决策引擎,使边缘设备能够利用外部计算资源,同时保持TCP/IP兼容性。外部决策引擎中的基于Transformer的DRL代理使用自注意力机制来捕捉时间依赖性,过滤噪声,并通过统一策略协调子流的控制。在模拟和真实双频Wi-Fi测试床上的广泛评估表明,TCCO在适应性和性能上均优于现有最先进基线,验证了TCCO在无线网络中的可行性和有效性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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