Machine Learning RF Optimization via Structured Interface
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Solution Overview
Problem
Optimizing radio frequency (RF) resources in base stations is a complex manual process, and collecting user experience data to support RF optimization efforts is challenging, hindering the maximization of base station value and network performance.
Innovation Solution
Implementing a machine-learning-based RF optimization system that employs a client-server architecture to collect reference signal data from mobile communication devices, using machine-learning algorithms to generate coverage maps and fine-granular clustering of cell towers and users, and automatically adjusting antenna tilts and power levels to optimize network performance and user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual RF optimization process is used, then engineering parameters can be adjusted, but the process becomes complex and time-consuming
Solution Approach 1:
The system enables self-service through automated RF optimization where the network device autonomously collects data, generates models, and adjusts parameters without manual intervention. The machine learning model automatically performs optimization tasks that previously required manual engineering effort, making the system serve itself.
Solution Approach 2:
The patent replaces the manual mechanical optimization process with an automated electronic system. Machine learning algorithms substitute for manual engineering analysis, and automated data collection replaces manual field measurements, transforming the optimization from a manual mechanical process to an automated electronic system.
2Reliability
If user experience data is collected for RF optimization, then network performance can be improved, but data collection becomes challenging
Solution Approach 1:
The network device performs multiple functions: it serves as both a base station for communication and a data collection point for optimization. By integrating these functions, the system uses existing infrastructure to collect user experience data without requiring separate dedicated measurement devices, thus reducing complexity.
Solution Approach 2:
The patent introduces an intermediary machine learning model that bridges data collection and network optimization. The model acts as a mediator that processes raw user experience data and translates it into actionable optimization insights, making the data collection process more manageable and effective.
3Productivity
If automated adjustments are implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The optimization system is segmented into distinct functional modules: data collection module, model generation module, and parameter adjustment module. This segmentation allows each component to be developed and optimized independently, managing overall system complexity while maintaining high productivity through automated operations.
Data Source
AI summary
A method is provided for obtaining reference signal measurements over a structured interface to support RF optimization via machine learning. The method, performed by a network device, includes identifying a target cluster of cell towers for a radio access network (RAN); generating a model for collecting RAN measurements from mobile communication devices in the target cluster; and sending the model via a structured reference point to client applications on the mobile communication devices. The model may direct collection of and sending of the RAN measurements by the client applications. The method may further include receiving, via the structured reference point, the RAN measurements from the client applications based on the model; and aggregating the RAN measurements to represent aspects of the target cluster based on the model.