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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2506.12795 |
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| _version_ | 1866918059987435520 |
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| author | Bennis, Mehdi |
| author_facet | Bennis, Mehdi |
| contents | Just like power, water and transportation systems, wireless networks are a crucial societal infrastructure. As natural and human-induced disruptions continue to grow, wireless networks must be resilient to unforeseen events, able to withstand and recover from unexpected adverse conditions, shocks, unmodeled disturbances and cascading failures. Despite its critical importance, resilience remains an elusive concept, with its mathematical foundations still underdeveloped. Unlike robustness and reliability, resilience is premised on the fact that disruptions will inevitably happen. Resilience, in terms of elasticity, focuses on the ability to bounce back to favorable states, while resilience as plasticity involves agents (or networks) that can flexibly expand their states, hypotheses and course of actions, by transforming through real-time adaptation and reconfiguration. This constant situational awareness and vigilance of adapting world models and counterfactually reasoning about potential system failures and the corresponding best responses, is a core aspect of resilience. This article seeks to first define resilience and disambiguate it from reliability and robustness, before delving into the mathematics of resilience. Finally, the article concludes by presenting nuanced metrics and discussing trade-offs tailored to the unique characteristics of network resilience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12795 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Resilient-native and Intelligent NextG Systems Bennis, Mehdi Emerging Technologies Artificial Intelligence Just like power, water and transportation systems, wireless networks are a crucial societal infrastructure. As natural and human-induced disruptions continue to grow, wireless networks must be resilient to unforeseen events, able to withstand and recover from unexpected adverse conditions, shocks, unmodeled disturbances and cascading failures. Despite its critical importance, resilience remains an elusive concept, with its mathematical foundations still underdeveloped. Unlike robustness and reliability, resilience is premised on the fact that disruptions will inevitably happen. Resilience, in terms of elasticity, focuses on the ability to bounce back to favorable states, while resilience as plasticity involves agents (or networks) that can flexibly expand their states, hypotheses and course of actions, by transforming through real-time adaptation and reconfiguration. This constant situational awareness and vigilance of adapting world models and counterfactually reasoning about potential system failures and the corresponding best responses, is a core aspect of resilience. This article seeks to first define resilience and disambiguate it from reliability and robustness, before delving into the mathematics of resilience. Finally, the article concludes by presenting nuanced metrics and discussing trade-offs tailored to the unique characteristics of network resilience. |
| title | Resilient-native and Intelligent NextG Systems |
| topic | Emerging Technologies Artificial Intelligence |
| url | https://arxiv.org/abs/2506.12795 |