UE Clustering Control for Wireless Quality by Service Type
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Solution Overview
Problem
Conventional technologies do not consider how UE clustering affects wireless quality in terminal stations, leading to potential suboptimal network performance.
Innovation Solution
A wireless communication system that includes a central station, base stations, and terminal stations, where the central station learns and optimizes UE clustering based on wireless quality, service information, and environmental data to ensure required quality of experience (QoE) is satisfied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If UE clustering is performed without considering wireless quality impact, then terminal stations can be grouped for network management, but wireless quality requirements may not be satisfied
Solution Approach 1:
The system implements feedback loops where terminal stations report wireless quality measurements to the base station, and the base station reports to the network management device. This feedback mechanism enables the system to adjust clustering configurations based on actual wireless quality performance, ensuring quality requirements are met while maintaining clustering benefits
Solution Approach 2:
The clustering configuration is made dynamic rather than static. The network management device continuously learns optimal clustering patterns based on reported wireless quality data and service information, allowing the system to adapt clustering assignments in real-time to maintain reliability while preserving adaptability
2Productivity
If wireless quality management is performed without UE clustering optimization, then quality monitoring is simple, but network performance is suboptimal
Solution Approach 1:
The network management device acts as an intermediary that handles the complex task of learning and determining optimal clustering configurations. By centralizing this intelligence, the patent improves network performance through optimized clustering while keeping the complexity managed at a single point rather than distributed across all base stations and terminal stations
Solution Approach 2:
The system employs machine learning algorithms that automatically learn optimal clustering patterns from reported data without requiring manual configuration. This self-service approach enables the system to improve network performance through continuous optimization while minimizing the operational complexity for network administrators
Data Source
AI summary
A wireless communication system according to the present disclosure comprises a central station, a base station, and a terminal station configured to enable wireless inter-terminal station communication. The terminal station transmits information regarding wireless quality and information regarding a service in use to the base station. The base station clusters the terminal station according to clustering control information received from the central station. Also, the base station transmits information received from the terminal station and information regarding a wireless environment of the terminal station to the central station. The central station learns clustering, based on information received from the base station, such that required wireless quality corresponding to the service in use by the terminal station is satisfied, and transmits information regarding learned clustering to the base station as the clustering control information.