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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.03163 |
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| _version_ | 1866914145298808832 |
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| author | Giwa, Oluwaseyi |
| author_facet | Giwa, Oluwaseyi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03163 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Discovery in Dynamic Fading Wireless Networks Giwa, Oluwaseyi Machine Learning Signal Processing Methodology 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. |
| title | Causal Discovery in Dynamic Fading Wireless Networks |
| topic | Machine Learning Signal Processing Methodology |
| url | https://arxiv.org/abs/2506.03163 |