AI-Driven Radio Access Network Configuration for Emergency Services
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Solution Overview
Problem
Existing cellular networks face challenges in ensuring reliable communication for priority networks used by first responders and emergency services, as well as in effectively broadcasting Wireless Emergency Alerts (WEA) due to issues like lack of network coverage and poor signal reception.
Innovation Solution
The implementation of a strategic artificial intelligence (AI)/machine learning (ML) framework that configures radio access network components for location-based services. This framework includes automated pre-provisioning of network component configurations, validation through performance indicator tracking, and updates based on feedback, leveraging LBS network element management tools for implementation and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated AI/ML framework is implemented for network configuration, then configuration efficiency and reliability are improved, but system complexity increases
Solution Approach 1:
The patent introduces an AI/ML-based recommendation module as an intermediary between network performance monitoring and configuration management. This module processes complex performance data and generates configuration recommendations, acting as a mediator that handles the complexity internally while presenting simplified interfaces to operators and automated systems.
Solution Approach 2:
The system implements self-service through automated configuration deployment based on AI/ML recommendations. The network management system automatically applies configuration settings to network elements without requiring manual intervention, enabling the system to service itself and reduce operational complexity.
2Productivity
If proactive configuration updates are deployed, then service effectiveness is improved, but risk of configuration errors increases
Solution Approach 1:
The patent implements a closed-loop feedback system where network performance is continuously monitored, AI/ML models generate configuration recommendations based on this feedback, configurations are deployed, and subsequent performance changes are measured. This feedback loop enables validation of configuration effectiveness and allows for corrective actions if errors occur, balancing proactive deployment with error mitigation.
Solution Approach 2:
The system performs preliminary actions by generating and validating configuration recommendations before actual deployment. The AI/ML module prepares configuration settings in advance based on predicted performance improvements, allowing for pre-validation and risk assessment before committing changes to the live network.
3Extent of automation
If AI/ML framework is implemented for network management, then automation level is improved, but computational resource requirements increase
Solution Approach 1:
The patent segments the automation framework into distributed components, with AI/ML recommendation modules deployed at multiple network locations including edge devices and centralized management systems. This segmentation allows computational tasks to be distributed across the network infrastructure, reducing the computational burden on any single element and utilizing available processing resources more efficiently.
Data Source
AI summary
A processing system including at least one processor may apply an input vector to a location-based services recommendation module implemented by the processing system, where the location-based services recommendation module includes at least one location-based service prediction model, and where the input vector includes first characteristics associated with a first cell site of a cellular network. The processing system may obtain an output of the location-based services recommendation module in response to the applying of the input vector, the output including a first plurality of values for a plurality of configurable settings of at least a first network element associated with the first cell site. The processing system may then configure the at least the first network element associated with the first cell site to apply at least one of the first plurality of values for at least one of the plurality of configurable settings.