Terminal Cell Search Training Data Classification
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Solution Overview
Problem
Conventional cell search methods in terminals are inaccurate in noise environments with varying signal strengths, leading to false cell determination and increased resource consumption.
Innovation Solution
A terminal using machine learning to generate and update cell search training data, classify cells into valid and false sets, and adjust a classification function to maintain consistent cell search performance across different noise environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a high correlation reference value is set for valid cell determination, then the probability of falsely determining a valid cell as a false cell is reduced, but the probability of losing candidate cells that are determined as valid cells is increased
Solution Approach 1:
The patent applies dynamics by making the correlation reference value adaptive rather than fixed. The reference value dynamically adjusts based on the received signal strength indicator (RSSI) measurements from multiple cells. When RSSI indicates strong signal conditions, a higher reference value is used to reduce false determinations. When RSSI indicates weak signal conditions, a lower reference value is used to prevent losing valid candidate cells. This dynamic adaptation resolves the contradiction between reducing false determinations and maintaining candidate cell detection.
Solution Approach 2:
The patent changes the parameter of correlation reference value based on communication environment conditions. By measuring RSSI and comparing it against threshold values, the system selects different reference value levels (first reference value for strong signals, second reference value for weak signals). This parameter change allows the system to optimize valid cell determination accuracy for each specific signal condition, resolving the trade-off between false determination reduction and candidate cell preservation.
2Measurement precision
If a low correlation reference value is set for valid cell determination, then the probability of determining false cell as a valid cell is increased, but the probability of losing candidate cells is reduced
Solution Approach 1:
The system dynamically adjusts the correlation reference value based on real-time RSSI measurements. Instead of using a permanently low reference value that would increase false determinations, the system only uses lower reference values when signal conditions warrant it. This dynamic approach maintains high detection accuracy while preventing false cell determinations in stronger signal environments.
Solution Approach 2:
The patent implements parameter changes by selecting between different correlation reference values based on RSSI conditions. When RSSI exceeds a threshold indicating strong signals, the system switches to a higher reference value to maintain reliability. When RSSI is below the threshold indicating weak signals, the system uses a lower reference value to maintain detection precision. This conditional parameter change resolves the contradiction between the two opposing requirements.
3Ease of operation
If a fixed correlation reference value is used for valid cell determination, then the determination process is simple, but the performance varies significantly in different communication environments
Solution Approach 1:
The patent transforms the static determination process into a dynamic one by introducing RSSI-based adaptation. The system automatically measures RSSI, compares it against thresholds, and selects appropriate reference values without requiring complex manual configuration. This dynamic process maintains operational simplicity while significantly improving adaptability to different communication environments, as the system self-adjusts based on actual signal conditions.
Solution Approach 2:
The system performs self-service by automatically adapting its determination criteria based on measured signal conditions. The terminal itself measures RSSI, determines appropriate reference values, and applies them without external intervention. This self-service mechanism maintains process simplicity while achieving environment-specific optimization, resolving the contradiction between simplicity and adaptability.
Data Source
AI summary
An operating method of a terminal configured to communicate with at least one of a plurality of cells includes generating first cell search training data for the plurality of cells, determining at least one training candidate cell based on the first cell search training data, updating a classification based on second cell search training data among the first cell search training data and network information about the terminal to obtain an updated classification, the second cell search training data corresponding to the at least one training candidate cell, and determining a valid cell among the plurality of cells based on the updated classification.