AI-Driven Radio Network Planning for 5G Site Selection
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Solution Overview
Problem
Conventional network planning for 5G networks is inefficient, requiring extensive manual effort, lacks scalability, and struggles with data integration and optimization, leading to complex and costly deployment processes that fail to meet the diverse requirements of various service types and user experiences.
Innovation Solution
A system and method utilizing artificial intelligence (AI) for automated network planning and deployment, incorporating a cloud-native architecture with radio application programming interfaces (APIs) to generate optimal site locations and configurations, leveraging crowdsourced data and strategic inputs for efficient 5G network deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional desktop-based tools are used for network planning, then manual control and detailed analysis are possible, but the process requires huge man-hours and lacks scalability
Solution Approach 1:
The patent replaces manual mechanical processes with an AI-based automated system. The AI engine automatically collects, processes, and analyzes network planning data, eliminating the need for engineers to manually handle desktop-based tools and significantly reducing the time and effort required for network planning tasks
Solution Approach 2:
The system enables self-service through automated data collection from multiple sources, automatic preprocessing, and AI-driven analysis that operates without continuous human intervention. The platform autonomously performs tasks such as data aggregation, cleaning, and initial analysis, allowing the network planning process to serve itself
2Device complexity
If traditional network planning methods are used, then detailed manual analysis is possible, but the process is complex and cumbersome with extensive paperwork
Solution Approach 1:
The AI-based platform serves multiple functions within a single unified system: data collection from diverse sources, automatic preprocessing, spatial query processing, simulation, and optimization. This multi-functional approach eliminates the need for separate tools and paperwork for each task, simplifying the overall planning process while maintaining comprehensive analysis capabilities
Solution Approach 2:
The patent introduces an AI-based intermediary platform that mediates between various data sources, analysis requirements, and decision-making processes. This intermediary automatically handles data integration, processing, and translation into actionable insights, reducing the complexity of direct interactions between engineers and multiple planning tools
3Adaptability or versatility
If conventional approaches are used for 5G network planning, then traditional service coverage is achieved, but the system cannot meet diverse requirements of different service types and user experiences
Solution Approach 1:
The system applies local quality by enabling different optimization criteria and service parameters for different geographical areas and service types. The AI engine can tailor network planning parameters such as bandwidth allocation, latency requirements, and coverage priorities to match local demands for eMBB, uRLLC, or mMTC services, ensuring each region receives customized planning that meets its specific requirements while maintaining overall network reliability
Data Source
AI summary
The present disclosure provides a system and a method radio network planning and deployment. The system provides an end to end (E2E) automation for network planning where nominals are auto generated using 4G crowdsource data. The system provides a strategy based nominal generation to cover key geographical areas. Further, the system enables auto validation of generated nominals based on strategy inputs and capacity inputs to generate an optimal list of site and cell configurations for network planning.