ML Signal Propagation Model for Automated Wi-Fi Optimization

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing solutions for optimizing Wi-Fi networks are labor-intensive, require manual configuration of floor plans, calibration of RF environments, and specialized expertise, leading to inefficiencies and inaccuracies in network performance optimization.

Innovation Solution

The use of a machine learning model trained with signal characteristic measurement values to build a signal propagation model for the wireless network operational area, allowing for automated network optimization and elimination of manual planning and calibration requirements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual configuration and calibration methods are used for network optimization, then specialized expertise and control are maintained, but labor intensity increases and efficiency decreases

Engineering Contradiction:

Improvenetwork optimization accuracyVSAvoidoptimization efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs self-configuration by automatically collecting signal strength data from multiple access points, generating floor plans, and optimizing network parameters without requiring manual intervention or specialized expertise, thereby resolving the contradiction between optimization accuracy and efficiency

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes (physical measurement, manual configuration) with automated computational processes (machine learning models, automatic data collection and processing), eliminating labor-intensive tasks while maintaining optimization reliability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual floor plan configuration is required, then network coverage can be optimized, but the process becomes time-consuming and labor-intensive

Engineering Contradiction:

Improvenetwork coverage optimizationVSAvoidplanning time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary automated data collection and processing before optimization begins, automatically gathering signal strength data from multiple access points and generating floor plans in advance, which eliminates time-consuming manual measurement and configuration processes

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates automated copies of network data and environments through digital signal collection and virtual floor plan generation, replacing the need for physical manual measurement and configuration processes, thereby reducing planning time while maintaining coverage optimization reliability

Inventive Principle:

Principle #26Copying

3Reliability

If specialized expertise is required for network optimization, then optimization accuracy is maintained, but accessibility and ease of deployment are reduced

Engineering Contradiction:

Improveoptimization accuracyVSAvoiddeployment simplicity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system enables self-service deployment by automatically collecting signal data, generating floor plans, and optimizing network parameters without requiring specialized expertise or manual configuration, making the process accessible to users without technical expertise while maintaining optimization accuracy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent introduces automated intermediary processes (machine learning models, automatic data processing systems) that mediate between raw signal data and optimization decisions, eliminating the need for specialized expertise while maintaining accurate optimization results

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If automated machine learning models are used, then efficiency and accessibility are improved, but model training complexity and data requirements increase

Engineering Contradiction:

Improveoptimization efficiencyVSAvoidmodel training complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20250113234A1Inter-technology and inter-frequency machine learning-propagation systems and methods

Publication Date: 2025.04.03 EKAHAU OY

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 machine learning model is trained based on signals transmitted in accordance with a first radio access technology (RAT), and the signal propagation model is built for signals transmitted in accordance with a second RAT using the trained machine learning model. In further examples, the machine learning model is trained based on signals transmitted within a first set of frequency bands, and the signal propagation model is built for signals to be transmitted over a second set of frequency bands using the trained machine learning model.