发表机构
Ericsson Research（爱立信研究）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对移动网络控制问题，提出基于世界模型的方法，通过历史数据训练模型预测行动影响，利用不确定性估计找最优配置，优化目标可动态改变。该方法在模拟闭环控制中有效，相比传统方法和强化学习提升了节能与服务质量平衡性能。
AI 中文摘要
移动网络日益复杂，需要智能动态控制策略进行高效节能管理。我们提出一种基于世界模型的网络控制方法，能自适应配置关键参数。该世界模型由历史数据训练，预测行动对未来网络状态的影响。控制器利用模型不确定性估计稳健找到最优网络配置变化，且优化目标可动态改变无需重新训练模型。我们在移动网络节能功能的模拟闭环控制中证明了该方法的有效性，结果显示与传统方法和强化学习方法相比，在平衡节能与服务质量方面性能有所提升。最后，我们展示了世界模型在真实网络数据上的性能，并评估了控制器在各种吞吐量约束下提出的反事实行动。
英文摘要
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.
Journal ref2026 IEEE/IFIP Network Operations and Management Symposium (NOMS)
