Antenna Path Study Using ML for Accurate Site-Based Network Planning
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Solution Overview
Problem
Typical path studies in industrial automation systems rely on outdated and incomplete site information, necessitating manual adjustments and lacking flexibility in optimizing antenna locations and designs.
Innovation Solution
Implementing machine learning-based path studies using data radios or web services to automate the process, considering multiple sources of information such as maps, satellite imagery, and GPS data to identify optimal communication paths and antenna parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual path studies are performed by human operators using outdated site information, then the process can be completed with simple tools, but the accuracy and reliability of the path study results deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically collecting and storing current site information (satellite imagery, GPS data, topography) in a database before the path study is conducted. This ensures that when the path study is performed, accurate and up-to-date site information is already available, eliminating the need to rely on outdated manual data.
Solution Approach 2:
The patent replaces the manual mechanical process of path study with an automated computer-based system. The system uses algorithms to process satellite imagery, GPS coordinates, and topography data to automatically determine optimal antenna locations and communication paths, substituting human operators with automated computational methods that provide higher accuracy.
2Productivity
If automated path studies are implemented using machine learning and multiple data sources, then the productivity and efficiency of the process improves, but the device complexity increases
Solution Approach 1:
The automated path study system is designed as a multi-functional platform that can perform multiple tasks: collecting site information from various sources (satellite imagery, GPS, topography databases), processing this data through machine learning algorithms, generating path study reports, and optimizing antenna placements. This universal system replaces multiple separate manual processes with a single integrated automated solution.
Solution Approach 2:
The system performs self-service by automatically collecting, processing, and analyzing site information without requiring manual intervention. The machine learning models autonomously process the data and generate path study recommendations, eliminating the need for human operators to manually gather and analyze site data, thereby significantly improving productivity.
3Reliability
If multiple data sources are integrated for path study analysis, then the reliability of communication path selection improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system introduces an intermediary component - a centralized database and processing platform - that acts as a mediator between multiple data sources (satellite imagery providers, GPS systems, topography databases) and the path study analysis engine. This intermediary standardizes and integrates data from various formats and sources, making it easier to process multiple data types reliably without increasing the complexity of individual data collection processes.
Data Source
AI summary
Method and system for automating a path study to establish a private network between an access point and a remote asset along an optimal path. A predictive model identifies potential communications paths between the access point and the remote asset based on site data. The potential communications paths each specifies at least one antenna parameter. The machine learning also includes selecting the optimal path from the potential communications paths based at least in part on the network performance requirements of the private network and predicted signal quality along the potential communications paths using the antenna parameter.