# Deep Adaptive Rate Allocation in Volatile Heterogeneous Wireless Networks

arXiv: https://arxiv.org/abs/2603.20926 · HTML: https://mlsanity.com/paper/2603.20926/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 21 Mar 2026.

## Builds on (most cited first, 3 of 3)

- [FALCON: Fast and Accurate Multipath Scheduling using Offline and Online Learning](https://mlsanity.com/paper/2201.08969/) (2022)
- [A Framework for Multiaccess Support for Unreliable Internet Traffic using Multipath DCCP](https://mlsanity.com/paper/1907.04567/) (2019)
- [Adaptive Cheapest Path First Scheduling in a Transport-Layer Multi-Path Tunnel Context](https://mlsanity.com/paper/2106.16003/) (2021)

## Abstract (arXiv)

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.