Node Placement Service for RF Network Planning
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Solution Overview
Problem
The placement of Radio Access Network (RAN) devices in large-scale networks is often inconsistent and sub-optimal due to varying weights given to different types of information by network management personnel, leading to inefficient resource deployment and potential interference issues.
Innovation Solution
A node placement service that uses network information to identify traffic profiles and calculate time values based on traffic volume and performance metrics, prioritizing geographic areas for RAN device placement to optimize RF network planning and minimize human influence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If network management personnel manually determine RAN device placement based on varying weights of different information types, then human expertise and flexibility are utilized, but inconsistency and sub-optimality occur in placement decisions
Solution Approach 1:
The system enables automated RAN device placement decisions by having the network management system itself perform the evaluation and selection process without human intervention. The system automatically determines traffic profiles, calculates time values, and identifies candidate geographic areas based on predefined criteria, eliminating the inconsistency of manual human decisions while preserving adaptability through configurable parameters.
Solution Approach 2:
The system transforms subjective human judgment into objective parameter-based decisions by calculating quantitative metrics such as time values based on traffic volume and performance metrics. By changing the decision-making parameters from subjective weights to objective calculated values, the system achieves both consistency and adaptability.
2Manufacturing precision
If automated algorithms are used to determine RAN device placement, then consistency and objectivity improve, but the ability to incorporate nuanced human judgment may be reduced
Solution Approach 1:
The system introduces an intermediary layer of automated analysis that processes network information and traffic patterns to generate objective placement recommendations. This intermediary algorithmic layer maintains consistency while preserving adaptability by incorporating multiple information types and allowing configuration of evaluation criteria to reflect different network management priorities.
3Measurement precision
If multiple types of network information are considered with varying weights, then comprehensive evaluation is achieved, but decision-making complexity increases
Solution Approach 1:
The system segments the complex decision-making process into distinct, manageable components: traffic profile identification, time value calculation, and candidate area selection. Each component processes specific types of information independently, reducing overall complexity while maintaining comprehensive evaluation through the structured combination of these segmented processes.
Solution Approach 2:
The system simplifies complex multi-criteria evaluation by transforming it into a standardized parameter framework where different information types are converted into comparable metrics such as time values. This parameter standardization reduces decision-making complexity while preserving evaluation accuracy through mathematically rigorous transformations.
4Productivity
If RAN devices are placed densely to accommodate increasing users, then network capacity improves, but interference between devices increases
Solution Approach 1:
The system applies local quality optimization by evaluating placement decisions at specific geographic locations rather than uniformly across the entire network. By analyzing traffic profiles and performance metrics for each candidate area independently, the system can place devices densely in high-traffic areas while maintaining appropriate spacing in lower-traffic areas, thus increasing overall capacity while minimizing interference.
Data Source
AI summary
A method, a device, and a non-transitory storage medium provide a node placement service. The node placement service may generate geo-bins pertaining to a radio access network device, a sector of the radio access network device, or a sub-sector. The node placement service may generate time values for the geo-bins based on network information associated with end devices and the geo-bins. The node placement service may also generate return on investment values for the geo-bins based on the network information. The node placement service may use the time values, the return on investment values, or both for radio frequency design of a geo-bin.