Sector-Specific Pathloss Estimation Using UE Data
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Solution Overview
Problem
Current cellular communication network planning and optimization techniques use the same path loss parameters across all sectors, leading to inaccurate and unrealistic estimations due to sector-specific variations, and rely on costly and time-consuming drive tests for calibration, which are also not continuously updated to account for environmental changes.
Innovation Solution
A method that receives path loss data from wireless communication units within the network, filters out unreliable data, and derives sector-specific path loss estimation values using location information, allowing for continuous updates without the need for drive tests, by generating and calibrating path loss reference values using actual data from user equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If drive tests are performed to obtain accurate pathloss data for individual sectors, then measurement precision is improved, but loss of time and loss of money increase significantly
Solution Approach 1:
The system uses existing pathloss measurements from user equipment to automatically calibrate sector-specific pathloss models without requiring external drive tests. The network self-updates its pathloss database using data already collected during normal operation, eliminating the need for separate calibration campaigns.
Solution Approach 2:
The system implements a feedback mechanism where pathloss measurements from UEs are continuously collected, processed to identify sector-specific characteristics, and used to update pathloss models. These updated models then improve future pathloss estimations, creating a continuous improvement loop without requiring repeated drive tests.
2Measurement precision
If drive tests are performed to obtain accurate pathloss data, then measurement precision is improved, but loss of money increases
Solution Approach 1:
The network automatically calibrates its own pathloss models using measurements from existing user equipment. This self-service approach eliminates the need to hire external teams to perform costly drive tests, converting a significant operational expense into an automated process using existing network resources.
Solution Approach 2:
The system uses pathloss measurements collected for their primary purpose (network operation and resource allocation) and simultaneously utilizes them for secondary purposes (pathloss model calibration). This multi-functionality extracts maximum value from existing measurements, eliminating the need for separate dedicated calibration tests.
3Device complexity
If the same pathloss parameters are used across all sectors, then device complexity is reduced, but measurement precision deteriorates due to sector-specific variations
Solution Approach 1:
The system divides the network coverage area into distinct sectors and assigns separate pathloss parameters to each sector. This segmentation allows the model to capture sector-specific propagation characteristics (such as urban, suburban, or rural environments) while maintaining a manageable level of complexity through systematic organization.
Solution Approach 2:
The system applies different pathloss parameters locally to each sector based on its specific propagation characteristics. Instead of using a single uniform model globally, each sector receives customized parameters that reflect its local environment, improving accuracy while the modular structure prevents exponential complexity growth.
4Loss of time
If pathloss data is not continuously updated, then loss of time is reduced, but reliability deteriorates due to environmental changes
Solution Approach 1:
The system continuously collects pathloss measurements from user equipment during normal network operation and continuously updates sector-specific pathloss models in the background. This continuous process ensures the pathloss database remains current with environmental changes without requiring periodic interruptions for dedicated update campaigns.
Solution Approach 2:
The system implements continuous feedback loops where pathloss measurements are constantly monitored, compared against model predictions, and used to automatically adjust sector-specific parameters. This ongoing feedback ensures the model adapts to environmental changes (new buildings, foliage growth, etc.) while maintaining reliable estimations without manual intervention.
Data Source
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AI summary
A method for deriving pathloss estimation values within a cellular communication network. The method comprises receiving pathloss data obtained from a plurality of wireless subscriber communication units located within the cellular communication network, receiving location information corresponding to the plurality of wireless communications units, associating received pathloss data with individual sectors within a coverage area of the cellular communication network based at least partly on the location information for the respective wireless communications unit, and deriving pathloss estimation values for individual sectors within the cellular communication network based at least partly on the received pathloss data.