Wi-Fi networks increasingly suffer from performance degradation caused by contention-based channel access, dense deployments, and largely self-managed operation among mutually interfering access points (APs). In this paper, we propose a Digital Twin (DT) framework that captures the essential spatial and temporal characteristics of wireless channels and traffic patterns, enabling the prediction of likely future network scenarios while respecting physical constraints. Leveraging this predictive capability, we introduce two analytically derived performance upper bounds-one based on Shannon capacity and the other on latency behavior under CSMA-CA (Carrier Sense Multiple Access with Collision Avoidance)-that can be evaluated efficiently without time-consuming network simulations. By applying importance sampling to DT-generated scenarios, potentially risky network conditions can be identified within large stochastic scenario spaces. These same performance bounds are then used to proactively guide a gradient-based search for improved network configurations, with the objective of avoiding imminent performance degradation rather than pursuing globally optimal but fragile solutions. Simulation results demonstrate that the proposed approach can successfully predict time-dependent network congestion and mitigate it in advance, highlighting its potential for predictive and preventive Wi-Fi network management.

翻译：Wi-Fi网络日益受到基于竞争的信道接入、密集部署以及相互干扰的接入点（AP）之间高度自管理操作所导致的性能下降问题困扰。本文提出一种数字孪生（DT）框架，该框架能够捕捉无线信道与流量模式的关键时空特征，在遵循物理约束的前提下实现对未来潜在网络场景的预测。基于此预测能力，我们推导出两个解析形式的性能上界——一个基于香农容量理论，另一个基于CSMA-CA（载波侦听多路访问/冲突避免）机制下的延迟行为——这两个上界无需耗时的网络仿真即可高效计算。通过对数字孪生生成的场景应用重要性采样，可在庞大的随机场景空间中识别潜在的高风险网络状态。随后，利用这些性能上界主动引导基于梯度的搜索，以寻找更优的网络配置方案，其目标在于避免迫近的性能劣化，而非追求全局最优但脆弱的解。仿真结果表明，所提方法能够成功预测随时间演进的网络拥塞并提前实施缓解，突显了其在预测性与预防性Wi-Fi网络管理中的应用潜力。