AI Core Network Management System for Dynamic Bandwidth Allocation
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Solution Overview
Problem
The complexity of cellular network architectures makes it slow and inconsistent to react to changes or anticipated changes in usage, relying heavily on manual human input for modifications and upgrades.
Innovation Solution
A core network management system that uses artificial intelligence and machine learning to analyze performance data from various data centers and client provisioning requests, modifying the network architecture to meet quality of service metrics by adjusting bandwidth and connections automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human administrators manually analyze and modify cellular network architecture, then modifications can be made with human judgment and oversight, but the process becomes slow and inconsistent
Solution Approach 1:
The core network management system performs self-service by automatically analyzing performance data, determining modifications, and implementing architecture changes without human intervention. The system monitors network usage patterns, predicts future needs, and autonomously adjusts network configuration to maintain consistent performance while responding quickly to changes.
Solution Approach 2:
The system implements continuous feedback loops where performance data from the cellular network is constantly collected, analyzed, and used to trigger automatic modifications. The feedback mechanism ensures that modifications are made based on actual network conditions and that the results are monitored to maintain consistency and reliability.
2Adaptability or versatility
If the cellular network architecture is highly complex to handle various usage scenarios, then the network can accommodate diverse requirements, but it becomes difficult to react quickly to changes
Solution Approach 1:
The network management system applies dynamics by making the network architecture flexible and adaptable through automated real-time adjustments. Instead of static manual reconfiguration, the system dynamically modifies network parameters, bandwidth allocation, and resource distribution based on current and predicted usage patterns, enabling quick response while maintaining complexity handling capabilities.
Solution Approach 2:
The system performs preliminary actions by predicting future network usage patterns and proactively making architectural modifications before actual usage changes occur. This anticipatory approach allows the network to be pre-configured for upcoming demands, reducing reaction time while maintaining the ability to handle diverse scenarios.
3Reliability
If manual human input is used for network modifications, then changes can be made with human expertise, but the process requires excessive administrator effort
Solution Approach 1:
The core network management system autonomously performs all modification tasks including performance analysis, decision-making, and implementation without administrator involvement. The system maintains high reliability by using sophisticated algorithms and machine learning models that replicate and exceed human expertise, while completely eliminating the need for manual input in day-to-day operations.
Solution Approach 2:
The system replaces the mechanical process of human administrators manually analyzing and configuring network parameters with an automated computational system. Machine learning models and algorithms substitute human cognitive processes, maintaining modification quality while eliminating administrator effort and involvement in routine operations.
Data Source
AI summary
Various arrangements for managing a core cellular network of a cellular network are presented herein. A core network management system can receive a provisioning request for a plurality of user equipment (UE). Performance data from a plurality of cellular network data centers can be obtained and analyzed. An architecture of the core cellular network can be modified based on a machine learning model based on analyzing the performance data and the provisioning request.