提出在线算法，实时识别无线网络结构变化。
Causal Discovery in Dynamic Fading Wireless Networks
- 基于序列回归与NOTEARS约束，实现动态网络因果推断。
- 发现检测延迟随网络规模线性增长，噪声平方增长，结构变化越大越快检测到。
- 适合研究无线网络可靠性或在线学习的工程师与研究者。
由于干扰、信道衰落和移动性的动态演化，传统静态因果模型难以适用。本文针对动态衰落无线环境中的因果推断挑战，提出一种基于序列回归的算法，并创新性地应用NOTEARS无环约束，支持高效在线更新。理论推导了检测结构变化所需延迟的上下界，明确量化其与网络规模、噪声方差及衰落严重程度的关系。蒙特卡洛仿真验证了理论结果：检测延迟随网络规模线性增加，随噪声方差平方增长，与结构变化幅度呈反平方关系。研究为设计鲁棒的在线因果推断机制提供了理论依据和实践指导，有助于在非平稳无线条件下维持网络可靠性。
原文摘要 · Abstract (English)
Dynamic causal discovery in wireless networks is essential due to evolving interference, fading, and mobility, which complicate traditional static causal models. This paper addresses causal inference challenges in dynamic fading wireless environments by proposing a sequential regression-based algorithm with a novel application of the NOTEARS acyclicity constraint, enabling efficient online updates. We derive theoretical lower and upper bounds on the detection delay required to identify structural changes, explicitly quantifying their dependence on network size, noise variance, and fading severity. Monte Carlo simulations validate these theoretical results, demonstrating linear increases in detection delay with network size, quadratic growth with noise variance, and inverse-square dependence on the magnitude of structural changes. Our findings provide rigorous theoretical insights and practical guidelines for designing robust online causal inference mechanisms to maintain network reliability under nonstationary wireless conditions.
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