Sequential Monte Carlo for Network Resilience Assessment and Control

TL;DR

This paper introduces a sequential Monte Carlo framework for assessing and controlling resilience in wireless networks, effectively estimating rare failure probabilities and enabling policy-driven recovery strategies.

Contribution

The work develops a novel SMC-based approach with multilevel splitting and adaptive control for resilience analysis and mitigation in networked systems.

Findings

SMC outperforms standard Monte Carlo in estimating rare failure probabilities.

The framework enables effective evaluation of mitigation policies.

Numerical results demonstrate improved accuracy and control in a wireless network case.

Abstract

Resilience is emerging as a key requirement for next-generation wireless communication systems, requiring the ability to assess and control rare, path-dependent failure events arising from sequential degradation and delayed recovery. In this work, we develop a sequential Monte Carlo (SMC) framework for resilience assessment and control in networked systems. Resilience failures are formulated as staged, path-dependent events and represented through a reaction-coordinate-based decomposition that captures the progression toward non-recovery. Building on this structure, we propose a multilevel splitting approach with fixed, semantically interpretable levels and a budget-adaptive population control mechanism that dynamically allocates computational effort under a fixed total simulation cost. The framework is further extended to incorporate mitigation policies by leveraging SMC checkpoints for policy evaluation, comparison, and state-contingent selection via simulation-based lookahead. A delay-critical wireless network use case is considered to demonstrate the approach. Numerical results show that the proposed SMC method significantly outperforms standard Monte Carlo in estimating rare non-recovery probabilities and enables effective policy-driven recovery under varying system conditions. The results highlight the potential of SMC as a practical tool for resilience-oriented analysis and control in future communication systems.