Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Sequential Monte Carlo for Network Resilience Assessment and Control

Onel Luis Alcaraz López

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.

Sequential Monte Carlo for Network Resilience Assessment and Control

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.

Paper Structure

This paper contains 23 sections, 21 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries of SMC
  3. Näive MC
  4. SMC
  5. SMC pseudocode
  6. Accuracy and cost
  7. SMC for Resilience Assessment & Control
  8. Simulation Design
  9. Reaction coordinate
  10. Hitting levels
  11. Population control
  12. Mitigation Policies
  13. Comparing Policies
  14. Online mitigation/control
  15. A Use Case Example
  16. ...and 8 more sections

Figures (3)

  • Figure 1: Illustration of the online mitigation/control procedure.
  • Figure 2: Estimated non-recovery probability $\Pr(\xi)$ versus the offered traffic rate $\Lambda$ for $\delta\in\{50,200\}$ ms and using MC and SMC simulation approaches.
  • Figure 3: Estimated non-recovery probability $\Pr(\xi)$ versus the standard deviation of the latent stress $\sigma_F$ with the proposed SMC-assisted reconfiguration framework and policy sets $\mathcal{U}$ of different dimensions. We set $\rho'=1/2$, $\kappa=1/2$, and $N'=25$. For the case of $|\mathcal{U}|=5$, we also plot the relative selection frequency of each policy for $\sigma_F\in\{0.45, 0.575, 0.8\}$.