Edge-Served Congestion Control for Wireless Multipath Transmission with a Transformer Agent

Multipath TCP is widely adopted to enhance connection quality-of-service by leveraging multiple network pathways on modern devices. However, the evolution of its core congestion control is hindered by the OS kernel, whose monolithic design imposes high development overhead and lacks the resource flexibility required for data-driven methods. Furthermore, inherent noise in network statistics induces a partial observability problem, which can mislead data-driven methods like Deep Reinforcement Learning. To bridge this gap, we propose Jazz, a system that re-architects multipath congestion control through a decoupled architecture that separates the decision-making ``brain'' from the in-kernel datapath, enabling it to operate on an external (edge) entity. At its core, Jazz employs a Transformer-based agent that processes sequences of historical observations to overcome the partial observability of single-step reinforcement learning. This allows it to learn and master fluctuating link conditions and intricate cross-path dependencies. Tested on a dual-band (5GHz/6GHz) Wi-Fi testbed, our implementation improves bandwidth efficiency by at least 2.85\% over conventional methods and maintains 96.2\% performance under 1\% packet loss, validating this design as a practical blueprint for agile network intelligence.