Propagation Model Optimization via Tile Reliability Analysis
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Solution Overview
Problem
Existing empirical propagation prediction models in mobile communications networks lack accuracy due to uncertainties in path loss prediction, particularly in varying terrains and environments, leading to suboptimal network planning and performance.
Innovation Solution
A method and system that optimize propagation prediction models by subdividing service areas into map tiles, predicting and measuring reliability, comparing predicted and measured reliability, and iteratively adjusting model parameters to enhance accuracy, incorporating land cover data and geomorphic regions for refined parameter adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing empirical propagation prediction models are used, then network planning can be performed, but the accuracy of propagation prediction is insufficient due to uncertainties in path loss prediction
Solution Approach 1:
The service area is divided into multiple map tiles, and each tile is further divided into geomorphic regions based on terrain characteristics. This segmentation allows the model to predict propagation parameters separately for each region, improving overall prediction accuracy by accounting for local variations in terrain and environment.
Solution Approach 2:
The patent applies different propagation prediction parameters for different geomorphic regions within the service area. Each region is assigned specific parameters based on its terrain characteristics, enabling the model to capture local quality differences and provide more accurate predictions for each location.
2Reliability
If more base stations are deployed to improve coverage, then radio coverage reliability improves, but network cost and redundancy increase
Solution Approach 1:
The patent uses measured signal strength data from actual deployments to feedback and refine the propagation prediction model. By comparing predicted and measured values, the model parameters are adjusted to better reflect real-world conditions, enabling more accurate coverage prediction without requiring excessive base stations.
Solution Approach 2:
The model parameters are dynamically adjusted based on measured propagation data and terrain characteristics. By optimizing these parameters for each geomorphic region, the system achieves more accurate coverage predictions, allowing for optimal base station placement without redundancy.
Data Source
AI summary
A propagation prediction model having adjustable parameters is optimized in a mobile communications network, by subdividing a service area into a plurality of map tiles, predicting a tile reliability from the model for each map tile, averaging the predicted tile reliability from all the map tiles to obtain a predicted average service area reliability, measuring a service area reliability for all the map tiles to obtain a measured service area reliability, comparing the predicted average service area reliability with the predicted average service area reliability, and adjusting the parameters of the model when the measured service area reliability differs from the predicted average service area reliability by a predetermined amount.