UE Position Optimization for Adaptive Base Station Testing
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Solution Overview
Problem
Creating and maintaining optimal propagation channels for base station testing is challenging due to the complexity and time-consuming nature of designing these channels, which requires extensive computing resources and is prone to changes over time, making it difficult to achieve desired performance metrics.
Innovation Solution
A base station testing system that utilizes a plurality of user equipments (UEs) and a UE adjustment component, controlled by processors, to determine and move UEs to optimal locations based on performance feedback, reducing the need for manual knowledge of beam and precoder information, and enabling automated optimization of UE positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual design and adjustment of UE locations is performed, then propagation channels can be created, but the process is time-consuming and requires extensive computing resources
Solution Approach 1:
The system enables automated determination of UE locations through performance feedback loops. The base station autonomously evaluates channel conditions and adjusts UE positions without requiring manual intervention, thereby reducing time and computing resource consumption while maintaining optimal propagation channels.
Solution Approach 2:
The system implements a feedback mechanism where performance metrics from the base station are continuously monitored and used to automatically adjust UE locations. This closed-loop approach enables rapid adaptation to changing conditions and eliminates the need for time-consuming manual design iterations.
2Reliability
If extensive computing resources are allocated for channel design, then desired performance metrics can be achieved, but resource consumption increases
Solution Approach 1:
The base station autonomously performs performance evaluation and UE location optimization using minimal external computing resources. The system leverages existing network infrastructure and algorithms to achieve desired performance metrics without requiring extensive external computational power.
Solution Approach 2:
The system applies optimization only when and where needed based on performance feedback, rather than continuously allocating maximum computing resources. This selective approach maintains reliability by focusing computational effort on critical adjustments while reducing overall resource consumption.
3Measurement precision
If manual knowledge of beam and precoder information is required, then accurate UE positioning can be achieved, but the operation becomes complex and difficult
Solution Approach 1:
The base station automatically determines optimal UE locations using its own beam and precoder information without requiring external manual input. The system leverages its existing knowledge base and performance feedback to autonomously achieve accurate positioning, thereby simplifying operation while maintaining precision.
Solution Approach 2:
The system introduces an automated control component that acts as an intermediary between the base station's internal knowledge and UE positioning. This component translates complex beam and precoder information into actionable location adjustments, maintaining accuracy while reducing operational complexity.
4Stability of the object's composition
If UE locations are fixed, then system stability is maintained, but the system cannot adapt when conditions change over time
Solution Approach 1:
The system transitions from static UE positioning to dynamic adjustment based on real-time performance feedback. UE locations are continuously optimized according to changing channel conditions, maintaining system stability through controlled adaptation rather than rigid fixation.
Solution Approach 2:
The automated feedback mechanism continuously monitors performance metrics and triggers UE location adjustments only when necessary. This approach maintains system stability during normal operation while enabling timely adaptation when conditions change, balancing both requirements effectively.
Data Source
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AI summary
A base station testing system includes a plurality of user equipments (UEs), a UE adjustment component, and one or more processors. The one or more processors determine respective initial locations of the plurality of UEs with respect to a base station and cause the UE adjustment component to move the plurality of UEs to the respective initial locations. The one or more processors determine one or more candidate locations of each UE with respect to the base station and cause the UE adjustment component to move each UE to the one or more candidate locations of the UE. The one or more processors then determine respective optimal locations of the plurality of UEs with respect to the base station and cause the UE adjustment component to move the plurality of UEs to the respective optimal locations.