Deep Adaptive Rate Allocation in Volatile Heterogeneous Wireless Networks

Abstract

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.

Figures (11)

• Figure 1: Scheduler response to antiphase path capacity variations. Fixed-rule schedulers maintain fixed allocation regardless of path capacity. Reactive schedulers exhibit observation-reaction lag $\tau$. DARA eliminates this lag through its predictive horizon, preemptively adjusting CWND fractions to match anticipated capacity variations.
• Figure 2: DARA's Predictive Rate Control Architecture. The Transformer predicts CWND and SRTT evolution for each path. The DQN policy uses these predictions to compute CWND fractions that rate-limit each path. In this example, DARA predicts Path 1 degradation and pre-emptively limits it to $\phi_1=0.3$, whilst allowing the stable path full utilisation at $\phi_2=1.0$, enabling sequential packet arrival and minimal head-of-line blocking.
• Figure 3: Path throughput normal distribution.
• Figure 4: Mean reward with 95% confidence intervals across all methods.
• Figure 5: Asymmetric burst pattern showing one complete 2.5-second cycle. Path 1 exhibits 700 ms bursts peaking at 10 Mbps, collapsing to 0.2 Mbps baseline; Path 2 maintains stable 1.8 Mbps throughout.