Line-of-Sight Signal Strength Prediction for Terrain and Foliage Blocking
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Solution Overview
Problem
Current signal strength prediction methods for wireless services, relying on statistical models, often result in inaccurate predictions due to insufficient granularity in reflecting line of sight (LOS) and non-LOS conditions, leading to a high error margin and increased installation failures, especially at cell edges where signal strength is low.
Innovation Solution
Implementing a point-to-point analysis that examines the actual LOS between an access point and customer premises equipment, using propagation path data, image data, and elevation data to determine foliage and terrain blocking, allowing for more accurate signal strength predictions tailored to specific locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If comprehensive statistical models are used to analyze large geographical areas, then coverage area is improved, but measurement precision deteriorates due to insufficient granularity in reflecting line of sight conditions
Solution Approach 1:
The patent divides the geographical area into discrete grid cells (e.g., 100m x 100m pixels) and performs line of sight analysis for each cell individually. This segmentation allows the system to maintain large coverage area while achieving high measurement precision by evaluating LOS conditions at each specific location rather than using averaged statistical data across large regions.
Solution Approach 2:
The patent applies local quality by performing point-to-point line of sight analysis tailored to each specific grid cell and customer premises location. Instead of using uniform statistical models across the entire geographical area, the system evaluates local terrain features, obstructions, and propagation conditions specific to each location, thereby achieving high prediction accuracy while maintaining broad coverage.
2Device complexity
If statistical models with large area analysis are used, then device complexity is reduced, but reliability deteriorates due to high error margin and increased installation failures
Solution Approach 1:
The patent performs preliminary line of sight analysis and signal strength prediction before customer premises equipment installation. By evaluating propagation conditions, terrain obstructions, and potential signal blockages in advance using point-to-point analysis, the system identifies suitable installation locations and predicts signal strength with high accuracy, thereby reducing installation failures and improving reliability without requiring complex real-time adjustment mechanisms.
3Measurement precision
If point-to-point line of sight analysis is implemented, then measurement precision is improved, but calculation time increases due to detailed propagation path examination
Solution Approach 1:
The patent segments the analysis into discrete grid cells and processes each cell independently using point-to-point line of sight evaluation. This segmentation enables parallel processing of multiple locations, reducing overall computation time while maintaining high measurement precision for each individual location. The systematic division of the geographical area into manageable units allows efficient resource utilization during analysis.
Data Source
AI summary
Architectures and techniques are presented that can provide point-to-point analysis to generate an improved signal strength prediction (SSP) based on, e.g., earth surface image data processing and analysis to draw conclusions of line of sight (LOS) along the propagation path between a BTS or another AP transmitter and CPE receiver. For example, USGS image data and/or elevation data of locations are identified to correspond to signal propagation between the transmitter and receiver can be analyzed for LOS signal quality at a fixed location, in addition to the statistical model prediction of the RF signal quality. As a result, foliage or terrain that obstructs the LOS can be identified and utilized to improve SSP by eliminating the additional pathloss due to LOS obstructions. Such can provide a significant improvement to SSP results that are conventionally predicted by statistical models rather than a point-to-point analysis.