Base Station Grouping Control Across Changing Traffic Periods
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Solution Overview
Problem
Existing base station system operation control methods struggle to accurately adjust operations across different period types, leading to increased processing load due to varying transition tendencies in base station systems, and classifying systems by similarity increases the number of groups, which is undesirable for reducing overall system load.
Innovation Solution
A control system that identifies degree-of-similarity between base station systems, classifies them into groups based on this similarity, and uses a leader-follower approach for operation control, predicting and adjusting operations based on a leader base station's degree of operation to reduce processing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If base station systems are classified into groups based on degree of similarity for each period type, then operation control accuracy is improved, but the number of base station system groups increases
Solution Approach 1:
The patent segments base station systems into multiple groups based on degree of similarity in operation transitions for each period type. This segmentation allows accurate matching of follower base stations with leader base stations that have similar operational characteristics, thereby improving operation control accuracy while managing system complexity through structured classification.
Solution Approach 2:
The patent implements dynamic classification where base station systems are re-grouped for each period type (e.g., daytime, nighttime, weekdays, weekends) based on their operational transition characteristics. This dynamic approach ensures that the grouping adapts to varying operational patterns across different periods, maintaining high control accuracy without requiring a single static complex classification structure.
2Measurement precision
If the number of base station system groups is increased to improve classification accuracy, then operation control precision is improved, but processing load increases
Solution Approach 1:
The patent applies partial action by selecting only the most relevant period types for classification based on operational needs. Instead of classifying for all possible period combinations, it focuses on significant periods where operational patterns differ, thereby achieving sufficient classification accuracy while reducing processing load associated with comprehensive multi-dimensional classification.
Solution Approach 2:
The patent changes the classification parameters dynamically based on period type. By adjusting which parameters are used for similarity assessment according to the current period (e.g., using different weightings for traffic patterns during daytime vs. nighttime), it achieves accurate classification without the computational burden of fixed high-dimensional classification across all periods.
3Measurement precision
If operation control is performed for each period type separately, then control accuracy is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal operation control framework that handles multiple period types through a common leader-follower mechanism. The same basic control logic is applied across different period types, with the universality achieved by reusing the leader-follower relationship model while adapting the similarity assessment to period-specific characteristics, thereby maintaining control accuracy without proportionally increasing system complexity.
Solution Approach 2:
The patent uses copying by replicating the leader-follower control structure across different period types. Instead of creating entirely separate control systems for each period, it copies the proven leader-follower mechanism and adapts it to period-specific operational patterns, reducing system complexity through reuse of the core control architecture while maintaining period-specific control accuracy.
Data Source
AI summary
A classification module classifies, for each period type, a plurality of base station systems into a plurality of base station system groups based on a degree of similarity identified for the each period type. An operation control module executes, for each of the plurality of base station system groups obtained in the classifying in the period type to which present belongs, operation control corresponding to a degree of operation of a leader base station system included in the base station system group, with respect to a follower base station system included in the base station system group.