Optimizing Reinforcement Learning Training over Digital Twin Enabled Multi-fidelity Networks
In this paper, we investigate a novel digital network twin (DNT) assisted deep learning (DL) model training framework. In particular, we consider a physical network where a base station (BS) uses several antennas to serve multiple mobile users, and a DNT that is a virtual representation of the physical network. The BS must adjust its antenna tilt angles to optimize the data rates of all users. Due to user mobility, the BS may not be able to accurately track network dynamics such as wireless channels and user mobilities. Hence, a reinforcement learning (RL) approach is used to dynamically adjust the antenna tilt angles. To train the RL, we can use data collected from the physical network a...
| Authors | Hanzhi Yu, Hasan Farooq, Julien Forgeat et al. |
|---|---|
| Primary category | cs.NI |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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