# Mobile Network Control with a World Model

arXiv: https://arxiv.org/abs/2607.17747 · HTML: https://mlsanity.com/paper/2607.17747/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [TD-MPC2: Scalable, Robust World Models for Continuous Control](https://mlsanity.com/paper/2310.16828/) (2023)
- [A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning](https://mlsanity.com/paper/2101.11246/) (2021)
- [An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression](https://mlsanity.com/paper/2504.18433/) (2025)
- [DRAG: Deep Reinforcement Learning Based Base Station Activation in Heterogeneous Networks](https://mlsanity.com/paper/1809.02159/) (2018)
- [Design and Evaluation of Deep Reinforcement Learning for Energy Saving in Open RAN](https://mlsanity.com/paper/2410.14021/) (2024)
- [Energy Saving in 6G O-RAN Using DQN-based xApp](https://mlsanity.com/paper/2409.15098/) (2024)
- [Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization](https://mlsanity.com/paper/2302.12899/) (2023)

## Built on by (most cited first, 1 of 1)

- [RAN-JEPA: An Action-Conditioned World Model for Energy-efficient Mobile Network Control](https://mlsanity.com/paper/2610.17584/) (2026)

## Abstract (arXiv)

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.