用世界模型动态调控移动网络，智能平衡节能与服务质量。
Mobile Network Control with a World Model
- 基于历史数据训练世界模型，预测参数调整对网络状态的影响。
- 利用模型不确定性找到节能与服务品质兼顾的最优配置。
- 可无需重训即切换优化目标，适合真实场景快速响应。
移动网络日益复杂，亟需智能、动态的控制策略以实现高效节能管理。本文提出一种基于世界模型的网络控制方法，能够自适应调整关键参数。世界模型从历史数据中训练，预测其动作对未来网络状态的影响。控制器利用模型的不确定性估计，稳健地搜索最优网络配置变化。此外，优化目标可动态调整而无需重新训练模型。我们在模拟闭环控制中验证了该方法在节能功能上的有效性，结果表明其在平衡节能与服务质量方面优于传统方法和强化学习方案。最后，我们使用真实网络数据评估了世界模型性能，并分析了控制器在不同吞吐量约束下提出的反事实动作。
原文摘要 · Abstract (English)
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.
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