5G Nominal Validation for Automated Site and Cell Planning
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Solution Overview
Problem
The conventional network planning for 5G networks is manual, tedious, and involves significant man-hours, with challenges in handling crowd-sourced data and geo-spatial datasets, leading to inefficient site planning and cell configuration.
Innovation Solution
A system and method for auto-validation of nominals using a Nominal Generation module and Nominal Validation module to generate and validate site locations and cell configurations based on capacity and strategy data, incorporating radio predictive algorithms for optimal site selection and cell configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual planning approach using desktop-based tools is used, then engineers can perform network planning, but the process becomes tedious and time-consuming with huge man-hours required
Solution Approach 1:
The patent replaces manual mechanical planning processes with an automated AI-based system. The AI model automatically processes crowd-sourced data, performs radio predictive tasks, and generates optimal site plans without requiring manual intervention in the computational processes, thereby eliminating tedious manual work and reducing time consumption
Solution Approach 2:
The system enables self-service automation where the AI model independently performs network planning tasks. The automated nominal validation module self-evaluates and self-optimizes site configurations without human intervention, allowing the system to serve itself in the planning process and significantly reducing the man-hours required
2Adaptability or versatility
If conventional manual planning is used, then site selection can be performed, but handling crowd sourced data and geo-spatial datasets becomes challenging
Solution Approach 1:
The patent replaces manual data handling with automated AI-based processing. The AI model automatically ingests, processes, and analyzes crowd-sourced data and geo-spatial datasets, performing complex data manipulation and spatial queries without human intervention, thereby improving data handling capability while reducing process complexity
Solution Approach 2:
The system introduces an AI model as an intermediary between raw data and planning decisions. This intermediary automatically processes complex data formats, performs radio predictive tasks, and generates optimized plans, thereby simplifying the overall planning process while enhancing adaptability to various data types
3Manufacturing precision
If multiple iterations are run to obtain optimal site plan, then coverage and capacity criteria can be met, but the process becomes cumbersome and complex
Solution Approach 1:
The patent implements automated feedback mechanisms where the AI model continuously evaluates site configurations against coverage and capacity criteria, automatically adjusts parameters, and iterates until optimal solutions are found. This automated feedback loop maintains planning precision while reducing the complexity of manual iteration management
Solution Approach 2:
The system replaces manual iterative planning with automated AI-based optimization. The AI model automatically performs multiple iterations of site configuration adjustments, evaluates performance against criteria, and converges on optimal solutions without human intervention, thereby maintaining precision while simplifying the iterative process
Data Source
AI summary
The disclosed system and method enables auto validation of initial nominals generated from capacity and strategy data sets to obtain an optimal list of sites and cell configurations. The disclosed system and method automates the process of nominal validation by providing a simple web interface on which requirements for a geography are received thus automating an entire process of ingesting huge crowd sourced data, geospatial data and doing predictions and analysis for obtaining the optimal sites.