Base Station Location Estimation Using Machine Learning
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Solution Overview
Problem
In modern wireless communication systems, determining the location of a base station by user equipment is challenging due to the complexity of multiple antennas and overlapping coverage areas, leading to inefficient measurements and reduced quality of service.
Innovation Solution
The user equipment measures reference signals from base stations at different positions, preprocesses the data, and provides it to a machine learning entity to estimate the base station location, which can then be used for improved network configuration and functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user equipment measures reference signals from all hearable base stations, then measurement completeness is improved, but processing complexity and energy consumption increase
Solution Approach 1:
The system performs preliminary actions by determining base station locations and storing them in advance. The location determination unit calculates geographic positions of base stations before measurement operations, and the measurement selection unit uses these pre-determined locations to filter which base stations require measurement, avoiding the need to measure all hearable base stations while maintaining measurement completeness for relevant stations.
Solution Approach 2:
The system segments the base station population into different groups based on their locations. The location determination unit divides base stations into relevant and irrelevant groups by comparing their positions against the user equipment's position and movement trajectory. This segmentation allows the measurement processing unit to focus only on base stations within the relevant segment, reducing overall processing complexity while maintaining measurement completeness for the relevant segment.
2Measurement precision
If user equipment measures reference signals from all hearable base stations, then measurement completeness is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary location determination and measurement selection before actual reference signal measurements. By pre-calculating which base stations are relevant based on location data and movement predictions, the system avoids energy-consuming measurements of irrelevant base stations, thereby reducing overall energy consumption while maintaining measurement completeness for relevant base stations.
Solution Approach 2:
The system segments base stations into measurement-worthy and non-measurement-worthy groups using location-based criteria. The measurement selection unit creates this segmentation by evaluating base station positions against the user equipment's current position and predicted movement path, ensuring that energy is consumed only for measuring base stations in the relevant segment while maintaining measurement completeness for that segment.
3Adaptability or versatility
If base station locations are unknown, then network configuration flexibility is maintained, but handover and beam switching decisions become less accurate
Solution Approach 1:
The system performs preliminary location determination of base stations before handover or beam switching decisions are needed. The location determination unit calculates and stores base station geographic positions in advance, enabling the measurement selection unit to make accurate handover decisions by comparing pre-determined locations with current user equipment position and movement predictions, thereby improving decision accuracy while maintaining network configuration flexibility.
Data Source
AI summary
An arrangement for determining a location of a base station by user equipment uses measurements of reference signals of a base station from different user equipment positions. The measurement results are preprocessed and then provided to a machine learning entity. The machine learning entity estimates the location of the base station and provides the estimated location for use in other functionality of the user equipment.