Sequential Monte Carlo for Resilient Networks: Assessment, Mitigation, and Generative Modeling

TL;DR

This paper introduces a sequential Monte Carlo simulation framework for assessing and improving the resilience of wireless networks against rare, cascading failures, using advanced probabilistic modeling and generative surrogates.

Contribution

It develops a novel SMC-based methodology for efficient rare-event estimation and resilience assessment, incorporating generative models for digital twins and policy evaluation.

Findings

SMC significantly outperforms standard Monte Carlo in rare-event probability estimation.

The framework effectively models staged degradation and recovery in wireless networks.

Generative sequence models enable data-driven simulation and policy testing.

Abstract

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.