aiqbench analysis
This research applies world models, a specific type of AI predictive architecture, to optimize mobile network control for energy efficiency without requiring retraining when objectives shift. By leveraging uncertainty estimates from the model, the approach robustly balances quality of service against power consumption in both simulated and real-world scenarios. The study demonstrates that this method outperforms traditional techniques and standard reinforcement learning baselines in dynamic network management tasks.
Mobile Network Control with a World Model
arXiv:2607.17747v1 Announce Type: cross Abstract: The increasing complexity of mobile networks necessitates intelligent and dynamic control strategies for efficient, energy-conserving management. We propose a world model-based approach for network control that enables adaptive configuration of crucial parameters. The world model is trained from historical data and predicts the impact of its actions on future network states. Our controller leverages the model's uncertainty estimate to robustly find optimal network configuration changes. Furthermore, the optimization objective can be changed dynamically without model retraining. We demonstrate the effectiveness of the approach in simulated closed-loop control of a mobile network energy-saving feature. Our results show improved performance in balancing energy savings with quality of service, compared to traditional methods and reinforcement learning approaches. Finally, we show the world model performance on real network data from, and evaluate counterfactual actions proposed by the controller under various throughput constraints.
Abstract:The increasing complexity of mobile networks necessitates intelligent and dynamic control strategies for efficient, energy-conserving management. We propose a world model-based approach for network control that enables adaptive configuration of crucial parameters. The world model is trained from historical data and predicts the impact of its actions on future network states. Our controller leverages the model's uncertainty estimate to robustly find optimal network configuration changes. Furthermore, the optimization objective can be changed dynamically without model retraining. We demonstrate the effectiveness of the approach in simulated closed-loop control of a mobile network energy-saving feature. Our results show improved performance in balancing energy savings with quality of service, compared to traditional methods and reinforcement learning approaches. Finally, we show the world model performance on real network data from, and evaluate counterfactual actions proposed by the controller under various throughput constraints.
Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
arXiv:2607.17747 [cs.NI]
(or arXiv:2607.17747v1 [cs.NI] for this version)
https://doi.org/10.48550/arXiv.2607.17747 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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