ML-Based 5G Private Network Deployment Recommendation
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Solution Overview
Problem
Selecting the right deployment scenario for a 5G private enterprise network is complex and time-consuming, often resulting in suboptimal solutions due to human-reliant processes.
Innovation Solution
A system and method using machine learning to infer an optimal 5G private network deployment scenario based on parameter values associated with an enterprise, such as security needs, cost considerations, and operational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-reliant processes are used to select deployment scenarios, then expertise and judgment can be applied, but the process becomes complex and time-consuming
Solution Approach 1:
The patent replaces the mechanical human decision-making process with an automated machine learning system. The ML model processes enterprise parameters and deployment scenario data to generate recommendations, eliminating the time-consuming manual analysis while maintaining or improving selection accuracy through data-driven insights.
Solution Approach 2:
The system enables self-service by allowing enterprises to input their own parameters and receive automated deployment recommendations without requiring extensive human intervention. The ML model independently processes the data and generates optimal scenario selections, reducing dependency on human experts for routine assessments.
2Reliability
If multiple deployment scenarios are evaluated in detail, then optimal solutions can be identified, but the operational complexity increases
Solution Approach 1:
The patent segments the complex evaluation process into distinct components: parameter extraction from enterprise data, feature engineering from deployment scenarios, ML model inference, and recommendation generation. This segmentation allows each component to be optimized independently and simplifies the overall system architecture.
Solution Approach 2:
The system transforms complex qualitative assessment into quantitative parameter evaluation. By converting deployment scenario characteristics into numerical features that the ML model can process, the system maintains evaluation thoroughness while reducing operational complexity through standardized parameter-based assessment.
3Measurement precision
If comprehensive parameter analysis is performed, then accurate recommendations can be made, but the processing complexity and time increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing enterprise parameters and deployment scenario data before the actual recommendation generation. Features are engineered and prepared in advance, allowing the ML model to focus on inference rather than raw data processing, thus improving both accuracy and efficiency.
Solution Approach 2:
The system replaces manual comprehensive analysis with automated ML-based processing. The machine learning model efficiently handles the analysis of multiple parameters simultaneously, achieving high recommendation accuracy without the time and complexity associated with manual comprehensive evaluation.
Data Source
AI summary
As described herein, a system, method, and computer program are provided for making a 5G private network deployment recommendation using machine learning. A plurality of parameter values associated with an enterprise for which a 5G private network is to be deployed are obtained. A machine learning model is used to infer, for the enterprise, an optimal deployment scenario for the 5G private network among a plurality of available deployment scenarios, based on the plurality of parameter values. An indication of the optimal deployment scenario is output as a recommendation for deploying the for the 5G private network for the enterprise.