Resilience and failure analysis in next-generation communication networks: A contemporary survey

Author Identifier (ORCID)

Wei Ni: https://orcid.org/0000-0002-4933-594X

Abstract

This paper provides a comprehensive exploration of network resilience in next-generation (xG) cellular communication infrastructures. It begins with an introduction to the network science framework and its relevance to modern communication systems, and then delves into the theoretical foundations of network resilience, including key concepts from graph theory, complex network analysis, and cascading failure models. A detailed examination of network failures in xG networks follows, employing graph-theoretic approaches to analyze failure propagation, identify critical nodes, and assess network vulnerabilities. The paper also outlines practical methodologies for enhancing resilience, such as adaptive topology design, failure prediction, and decentralized architectural frameworks. The paper further discusses future research directions, emphasizing emerging challenges and opportunities in network resilience. It also reviews ongoing standardization efforts aimed at integrating network science principles into communication infrastructure design. Lessons learned and open challenges are summarized that require further investigation, making it a valuable resource for researchers, engineers, and practitioners seeking to advance the resilience and adaptability of xG networks.

Document Type

Journal Article

Date of Publication

1-1-2025

Publication Title

IEEE Transactions on Network Science and Engineering

Publisher

IEEE

School

School of Engineering

Funders

Portuguese Foundation for Science and Technology (UIDP/UIDB/04234/2020) / Laboratório Associado de Sistemas Inteligentes (LA/P/0104/2020)

Comments

Bi, S., Yuan, X., Hu, S., Li, K., Ni, W., Hossain, E., & Wang, X. (2025). Resilience and failure analysis in next-generation communication networks: A contemporary survey. IEEE Transactions on Network Science and Engineering, 13, 2793–2821. https://doi.org/10.1109/TNSE.2025.3620950

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Link to publisher version (DOI)

10.1109/TNSE.2025.3620950