Radio Network Failure Remediation Using Generative AI
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Solution Overview
Problem
Conventional network failures often require human intervention and result in high Mean Time to Repair (MTTR) and inconsistent repair times due to unpredictable travel conditions, leading to increased costs and service disruptions.
Innovation Solution
Implementing AI/ML mechanisms for radio network operation automation using collected network information, including UAVs to gather data, and generative AI processes to autonomously generate remediation solutions for network failures, reducing the need for human intervention and optimizing repair processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human intervention is used for network failure recovery, then repair can be performed with existing expertise, but Mean Time to Repair (MTTR) increases and repair time becomes inconsistent
Solution Approach 1:
The system implements self-service through automated failure detection, root cause analysis, and remediation execution. The AI/ML engine autonomously analyzes failure data, identifies root causes, selects appropriate remediation actions from playbooks, and executes repairs without human intervention, enabling the network to self-heal and dramatically reducing MTTR
Solution Approach 2:
The system performs preliminary action by pre-configuring remediation playbooks with known failure patterns and their corresponding solutions. Historical failure data is collected and analyzed in advance to build knowledge bases that enable rapid automated response when failures occur, eliminating the need for human diagnostic time
2Ease of repair
If field trucks are dispatched for repairs, then on-site remediation can be performed, but travel and traffic conditions cause unpredictable and extended repair times
Solution Approach 1:
The system replaces the mechanical field truck dispatch mechanism with an automated digital system. Instead of physically traveling to sites, the AI/ML engine remotely detects failures, analyzes them using collected network data, and executes remediation actions through automated interfaces, eliminating travel time and traffic-related delays entirely
Solution Approach 2:
The system introduces an intermediary AI/ML engine that acts as a mediator between failure detection and remediation execution. This intermediary autonomously processes failure data, consults knowledge bases, and coordinates repairs without requiring human field technicians, thereby eliminating the need for physical travel while maintaining repair effectiveness
3Reliability
If more human resources are allocated for network monitoring and repair, then failure response capability improves, but operational costs increase
Solution Approach 1:
The system eliminates the need for continuous human monitoring by implementing self-service through automated failure detection and remediation. The AI/ML engine continuously monitors network health, automatically responds to failures, and learns from historical data, replacing human operational resources while maintaining or improving response capability
Solution Approach 2:
The system implements feedback mechanisms where failure data, remediation outcomes, and performance metrics are continuously collected and fed back to the AI/ML engine. This feedback loop enables the system to learn from past failures, improve its diagnostic accuracy, and optimize remediation strategies over time, reducing the need for additional human resources while enhancing response capability
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining historic failure data indicative of a plurality of past failures associated with a wireless communications network; obtaining historic remediation data indicative of a plurality of past remediation attempts associated with the plurality of past failures; obtaining current failure data indicative of a current failure associated with the wireless communications network; applying the historic failure data, the historic remediation data, and the current failure data to a generative artificial intelligence (AI) process, wherein the generative AI process outputs a proposed remediation solution to the current failure, and wherein the proposed remediation solution differs from each of the plurality of past remediation attempts; and running one or more simulations of application of the proposed remediation solution to the current failure, wherein the running of the one or more simulations provides a simulation result. Other embodiments are disclosed.