Signal Propagation Model for Wireless Network Optimization
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Solution Overview
Problem
Existing solutions for optimizing Wi-Fi networks are labor-intensive, require manual configuration of floor plans, calibration of wall attenuation values, and specialized expertise, leading to inefficiencies and potential errors in network optimization.
Innovation Solution
A system that uses data from a wireless network operational area to train a machine learning model, which builds a signal propagation model to optimize network performance, generate visualizations, and provide recommendations for improving coverage and capacity without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration of floor plans and calibration of wall attenuation values is performed, then network optimization accuracy is improved, but labor intensity and time requirements increase
Solution Approach 1:
The system performs self-service by automatically extracting floor plan information and calculating wall attenuation values from images and signal measurements without requiring manual user input. The machine learning model processes images to identify walls and obstacles, automatically configuring the floor plan and eliminating the need for manual configuration while maintaining optimization accuracy.
Solution Approach 2:
The patent replaces manual mechanical processes (manual floor plan configuration and wall attenuation calibration) with automated electronic systems. Machine learning models process images and signal data to automatically determine spatial relationships and attenuation values, substituting human expertise with computational algorithms that achieve similar or superior accuracy.
2Reliability
If specialized network planning expertise is required, then network optimization quality is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The system eliminates the need for specialized expertise by performing self-service through automated machine learning models. The platform automatically processes images, extracts spatial information, calculates attenuation values, and generates optimization recommendations without requiring user knowledge of network planning concepts, making the system accessible to end users.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that bridge the gap between raw image data and network optimization decisions. These models act as intelligent mediators that automatically interpret visual information, spatial relationships, and signal characteristics to produce optimization recommendations, eliminating the need for users to directly engage with complex network planning concepts.
3Productivity
If automated machine learning model training is used, then manual effort is reduced, but data processing complexity increases
Solution Approach 1:
The patent segments the complex data processing task into distinct modular components: image processing module for extracting spatial information, signal measurement module for capturing wireless characteristics, machine learning model for integrating data and generating predictions, and optimization recommendation module for producing actionable insights. This segmentation makes the overall system more manageable and easier to implement.
Solution Approach 2:
The machine learning model serves as an intermediary that simplifies the relationship between complex input data (images and signal measurements) and optimization recommendations. The model processes and integrates diverse data types, transforming them into meaningful predictions about signal propagation and network performance, thereby reducing the operational complexity for users.
Data Source
AI summary
The devices, systems, and methods described herein are directed to using data associated with a wireless network operational area to train a machine learning model, where the data includes one or more signal characteristic measurement values. The trained machine learning model is used to build a signal propagation model for the wireless network operational area. In some examples, the signal propagation model may be used to generate a network performance visualization or a heatmap of the wireless network operational area. In further examples, the system may generate one or more recommendations to optimize one or more performance indicators of the wireless network.