Deep Adaptive Rate Allocation in Volatile Heterogeneous Wireless Networks
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 fra...
| Authors | Gregorio Maglione, Veselin Rakocevic, Markus Amend et al. |
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| Primary category | cs.IT |
| Published | 21 March 2026 |
| Last updated at source | 21 March 2026 |
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