Modern multi-access 5G+ networks provide mobile terminals with additional capacity, improving network stability and performance. However, in highly mobile environments such as vehicular networks, supporting multi-access connectivity remains challenging. The rapid fluctuations of wireless link quality often outpace the responsiveness of existing multipath schedulers and transport-layer protocols. This paper addresses this challenge by integrating Transformer-based path state forecasting with a new multipath splitting scheduler called Deep Adaptive Rate Allocation (DARA). The proposed scheduler employs a deep reinforcement learning engine to dynamically compute optimal congestion window fractions on available paths, determining data allocation among them. A six-component normalised reward function with weight-mediated conflict resolution drives a DQN policy that eliminates the observation-reaction lag inherent in reactive schedulers. Performance evaluation uses a Mininet-based Multipath Datagram Congestion Control Protocol testbed with traces from mobile users in vehicular environments. Experimental results demonstrate that DARA achieves better file transfer time reductions compared to learning-based schedulers under moderate-volatility traces. For buffered video streaming, resolution improvements are maintained across all tested conditions. Under controlled burst scenarios with sub-second buffer constraints, DARA achieves substantial rebuffering improvements whilst state-of-the-art schedulers exhibit near-continuous stalling.

翻译：现代多接入5G+网络为移动终端提供了额外容量，提升了网络稳定性与性能。然而，在车联网等高移动性环境中，支持多接入连接仍具挑战性。无线链路质量的快速波动往往超过现有多路径调度器及传输层协议的响应速度。本文通过将基于Transformer的路径状态预测与新型多路径分割调度器——深度自适应速率分配（DARA）相结合，应对这一挑战。该调度器采用深度强化学习引擎，动态计算可用路径上的最优拥塞窗口占比，从而确定数据分配策略。一种包含六项归一化奖励函数且通过权重介导冲突消解的机制驱动DQN策略，消除了反应式调度器固有的观测-响应延迟。性能评估基于Mininet搭建的多路径数据报拥塞控制协议测试平台，并采用车联网环境中移动用户的轨迹数据。实验结果表明，在中度易变轨迹场景下，DARA相比基于学习的调度器实现了更优的文件传输时间缩减。针对缓冲视频流，该方案在所有测试条件下均能维持分辨率提升。在亚秒级缓冲区约束的受控突发场景中，DARA实现了显著的再缓冲性能改善，而现有最优调度器则呈现近乎持续的播放卡顿现象。