Local Path Loss Model for Mobile Location
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Solution Overview
Problem
Current mobile location systems using signal level measurements face challenges due to path loss models being general and costly to implement, with significant deviations caused by environmental changes, user behavior, and measurement inaccuracies, leading to inaccurate location calculations.
Innovation Solution
A method to obtain a local path loss versus range model by measuring path loss and range in a radio communications network, using a model of the form Lp(r)=β+α*10*log 10 (r), where β is a non-range dependent term and α represents range dependence, allowing for more accurate location calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a detailed survey is performed to populate a database for local path loss modeling, then location precision is improved, but cost and effort increase significantly
Solution Approach 1:
The mobile terminal performs self-measurement of path loss values using its own signal level measurements and calculated ranges, eliminating the need for external surveyors to collect data. The terminal autonomously gathers the data needed to characterize local propagation conditions.
Solution Approach 2:
The system uses feedback from multiple mobile terminals reporting their measured path loss values and locations to iteratively refine and update the path loss model for the area. This continuous feedback mechanism allows the model to adapt to changing conditions without requiring complete re-surveying.
2Device complexity
If a general path loss model is used for location calculations, then device complexity is reduced, but location precision deteriorates due to significant deviations from actual measurements
Solution Approach 1:
The patent transitions from using a single general path loss model for the entire service area to creating location-specific path loss models. Each model is tailored to the local propagation conditions of a specific area, capturing the unique characteristics of that environment rather than applying a universal approximation.
Solution Approach 2:
The path loss model is made dynamic and adaptable rather than static. The model can be updated and refined based on actual measurements from mobile terminals, allowing it to adapt to changing propagation conditions over time without requiring a complete database re-survey.
3Measurement precision
If extensive database surveys are conducted to capture local path loss characteristics, then location accuracy is improved, but the system becomes difficult to maintain and update
Solution Approach 1:
The system establishes a feedback mechanism where mobile terminals continuously report their measured path loss values and locations. This feedback loop enables the system to detect and account for changes in propagation conditions over time, such as new buildings or seasonal variations, without requiring manual re-surveying.
Solution Approach 2:
The system performs self-updating through automated collection and processing of measurement data from mobile terminals. The path loss models are automatically refined based on actual usage data, eliminating the need for manual maintenance and updates by network operators.
Data Source
AI summary
A method for obtaining a local path loss versus range model in a radio communications network is disclosed. The method comprises obtaining at least one path loss measurement and an associated range measurement at an approximated location of a mobile radio terminal within the radio communications network and applying this to a path loss versus range model to obtain the local path loss versus range model. The local model may also be used to obtain a more accurate location of the mobile radio terminal within the radio communications network.