Sequential Monte Carlo for Resilient Networks: Assessment, Mitigation, and Generative Modeling
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914534504005632 |
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| author | López, Onel L. A. Azarbahram, Amirhossein |
| author_facet | López, Onel L. A. Azarbahram, Amirhossein |
| contents | Resilience is becoming crucial for future wireless networks, which must withstand, adapt to, and recover from rare but potentially cascading disruptions. This paper develops a sequential Monte Carlo (SMC) simulation framework for such systems, in which resilience failures are formulated as path-dependent rare events arising from staged degradation and delayed recovery, and are decomposed into semantically interpretable levels defined by a reaction coordinate. Building on this structure, we present a fixed-level splitting approach with budget-aware population control, enabling efficient estimation of rare non-recovery probabilities. We discuss the potential reuse of SMC checkpoints as representative near-critical states for policy evaluation and simulation-based selection. We further extend the methodology to learned stochastic simulation by using generative sequence models as restartable surrogates within data-driven digital twins. We showcase the framework in a delay-critical wireless network use case, where SMC substantially improves over standard Monte Carlo in rare-event regimes with both physical and learned simulators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04751 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sequential Monte Carlo for Resilient Networks: Assessment, Mitigation, and Generative Modeling López, Onel L. A. Azarbahram, Amirhossein Systems and Control Resilience is becoming crucial for future wireless networks, which must withstand, adapt to, and recover from rare but potentially cascading disruptions. This paper develops a sequential Monte Carlo (SMC) simulation framework for such systems, in which resilience failures are formulated as path-dependent rare events arising from staged degradation and delayed recovery, and are decomposed into semantically interpretable levels defined by a reaction coordinate. Building on this structure, we present a fixed-level splitting approach with budget-aware population control, enabling efficient estimation of rare non-recovery probabilities. We discuss the potential reuse of SMC checkpoints as representative near-critical states for policy evaluation and simulation-based selection. We further extend the methodology to learned stochastic simulation by using generative sequence models as restartable surrogates within data-driven digital twins. We showcase the framework in a delay-critical wireless network use case, where SMC substantially improves over standard Monte Carlo in rare-event regimes with both physical and learned simulators. |
| title | Sequential Monte Carlo for Resilient Networks: Assessment, Mitigation, and Generative Modeling |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2605.04751 |