5G RAN Quarantine Control for Network Slice Outage Resiliency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless networks face challenges in mitigating network outages due to equipment failure, severe weather, or malicious attacks while maintaining minimal quality of service, exacerbated by imperfect Fault-Attack-Failure-Outage (FAFO) intelligence and strict application limitations by Mobile Network Operators.
Innovation Solution
Implementing a Quarantine Control Network (QCN) with AI modeling for FAFO intelligence to determine containment and restoration, using Sentinels to manage network resources and adjust for existing UEs and services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network resources are allocated for quarantine control during FAFO events, then network resiliency is improved, but device complexity increases
Solution Approach 1:
The network is segmented into multiple layers (radio access network, transport network, core network) with distributed quarantine control functionality. Each layer has autonomous sentinels that can detect and respond to FAFO events independently, eliminating the need for a single complex centralized quarantine control system while improving overall network resiliency through layered defense.
Solution Approach 2:
The patent implements self-service mechanisms where network elements automatically detect FAFO events, isolate affected resources, and restore services without human intervention. Sentinels autonomously monitor network health, trigger quarantine procedures when anomalies are detected, and coordinate restoration actions, reducing the complexity of manual network management while enhancing reliability.
2Measurement precision
If AI modeling is used for FAFO intelligence to determine containment and restoration, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces an AI modeling intermediary layer that sits between raw network data and quarantine control decisions. This intermediary processes FAFO intelligence data, patterns, and analytics to provide actionable insights without requiring complex AI algorithms to be embedded in every network device. The AI layer translates complex patterns into simple control signals for network elements.
Solution Approach 2:
The AI modeling framework is designed as a universal platform that serves multiple functions: detecting FAFO events, analyzing attack patterns, determining containment strategies, and coordinating restoration across different network layers. This multi-functional approach consolidates what would otherwise require multiple separate complex systems into a single versatile AI infrastructure.
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
A system is disclosed for quarantining and recovery of a network after an outage or in advance of a potential outage. Unaffected network slices are isolated and recovery is initiated by quarantine physical and virtual network functions. An AI model is trained based on recent events detected by sensors disposed throughout the network to determine whether to quarantine network slices, move services to unaffected network slices, or initiate recovery. Once the network is stabilized, resources that are specifically allocated for recovery and services are released and the traffic moved back to the recovered network slices.