5G Coverage Prediction Using 4G MR Data Differences
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Solution Overview
Problem
Existing 5G network coverage prediction methods using 4G network stations are inaccurate due to limitations in propagation models and electronic maps, leading to low prediction accuracy.
Innovation Solution
A method and device for predicting 5G network coverage by determining the first coverage strength of a 4G network, calculating the coverage strength difference with a 5G network using MR data and characteristic information, and predicting the 5G network coverage based on this difference without relying on propagation models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional propagation model-based method is used for 5G network coverage prediction, then the prediction can be implemented, but the accuracy is low due to model limitations and electronic map constraints
Solution Approach 1:
The patent segments the coverage prediction problem into two parts: (1) using measured MR data from the first network to establish actual coverage characteristics, and (2) using characteristic information differences to transfer these characteristics to the second network. This segmentation avoids relying on a single propagation model while improving accuracy.
Solution Approach 2:
The patent changes the approach from using absolute propagation model parameters to using relative characteristic information differences between networks. By focusing on parameter differences rather than absolute values, the method reduces errors accumulated from model limitations and electronic map inaccuracies.
2Ease of operation
If propagation model and electronic map are used for coverage prediction, then theoretical calculation can be performed, but calculation errors accumulate reducing accuracy
Solution Approach 1:
The patent introduces MR data as an intermediary that bridges the gap between theoretical models and actual coverage. Instead of directly using propagation models to predict coverage, the method uses measured MR data from a similar network as an intermediary reference, thereby reducing the impact of model errors.
Solution Approach 2:
The patent copies the coverage characteristics from the first network (which has measured MR data) to the second network by applying characteristic information differences. This copying approach avoids direct reliance on error-prone propagation models while maintaining calculation feasibility.
Data Source
AI summary
Disclosed are a network coverage prediction method and device, and a non-transitory computer readable storage medium. The network coverage prediction method may include: acquiring Measure Report (MR) data of a first network, first characteristic information corresponding to the first network, and second characteristic information corresponding to a second network, where the first network and the second network have a same coverage area, and are reusable and co-sited; determining a first coverage strength of the first network in the coverage area according to the MR data; determining a coverage strength difference between the first network and the second network in the coverage area according to the first characteristic information and the second characteristic information; and determining a predicted coverage strength of the second network in the coverage area according to the first coverage strength and the coverage strength difference.