Transformer-Based Multipath Congestion Control: A Decoupled Approach for Wireless Uplinks
Zongyuan Zhang, Tianyang Duan, Liang Wang, Zihan Fang, Zheng Lin, Yijun Lu, Jiening Wu, Xia Du, Miao Yang, Zhe Chen, Heming Cui, Jun Luo

TL;DR
This paper introduces TCCO, a Transformer-based framework for multipath congestion control that improves wireless uplink performance by leveraging external decision engines and self-attention mechanisms.
Contribution
It presents a decoupled architecture with a Transformer-based DRL agent for multipath congestion control, enhancing adaptability and performance in wireless networks.
Findings
TCCO outperforms existing baselines in simulated environments.
The framework maintains TCP/IP compatibility while improving control.
Extensive tests show TCCO's effectiveness on real Wi-Fi testbeds.
Abstract
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…
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Taxonomy
TopicsWireless Networks and Protocols · Network Traffic and Congestion Control · IPv6, Mobility, Handover, Networks, Security
