mlsanityData to 14 Oct 2026

Mobile Network Control with a World Model

arXiv 2607.17747 ↗First posted 20 Jul 2026cs.NI cs.AIcited by 1

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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.

Key facts. Cited by 1 paper 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

11 of 32 references.

References · 11

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2018World Modelscited by 1,513
  3. 2023Mastering Diverse Domains through World Modelscited by 1,253
  4. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 774
  5. 2023TD-MPC2: Scalable, Robust World Models for Continuous Controlcited by 512
  6. 2021A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learningcited by 15
  7. 2025An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regressioncited by 9
  8. 2018DRAG: Deep Reinforcement Learning Based Base Station Activation in Heterogeneous Networkscited by 6
  9. 2024Design and Evaluation of Deep Reinforcement Learning for Energy Saving in Open RANcited by 3
  10. 2024Energy Saving in 6G O-RAN Using DQN-based xAppcited by 2
  11. 2023Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimizationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026RAN-JEPA: An Action-Conditioned World Model for Energy-efficient Mobile Network Controlcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Reinforcement Learning with Reward Machines for Sleep Control in Mobile Networks
  2. 2026Temporally Consistent Graph Q-Networks for Intelligent Network Control
  3. 2024Continual Model-based Reinforcement Learning for Data Efficient Wireless Network Optimisation
  4. 2026Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control
  5. 2026Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G
  6. 2025Multi-Task Lifelong Reinforcement Learning for Wireless Sensor Networks
  7. 2026ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems
  8. 2025World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks