Usage Category-Specific Self-Organizing Network Optimization
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Solution Overview
Problem
Current self-organizing network (SON) processes struggle to effectively manage diverse usage categories in wireless communication networks, leading to skewed optimization results that can detrimentally affect performance for mobile broadband users due to the influence of Machine-Type Communication (MTC) devices such as drones and stationary devices, which require tailored optimization actions.
Innovation Solution
Implementing a usage category-specific SON system that identifies and separates metrics based on data type, movement type, and user type for UE devices, enabling differentiated optimization triggers and actions by determining usage categories through device identifiers and behavior patterns, and selecting appropriate optimization actions for base stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a general self-organizing network process is used to manage all devices, then the system complexity is reduced and ease of operation is improved, but the optimization precision deteriorates because MTC devices and mobile broadband users have different requirements that cannot be adequately addressed by a single optimization process
Solution Approach 1:
The patent segments the network optimization process by creating separate optimization processes for different usage categories. The system identifies usage categories (e.g., MTC devices, mobile broadband users) and applies category-specific optimization parameters and algorithms to each group, allowing precise optimization for each segment while maintaining overall system manageability through automated categorization.
2Manufacturing precision
If usage category-specific optimization is implemented, then the optimization precision is improved by addressing specific requirements of different device types, but the device complexity increases due to the need for usage category identification and separate optimization processes
Solution Approach 1:
The system employs self-service mechanisms where the network automatically identifies usage categories based on device behavior patterns and metrics without requiring manual intervention. The self-organizing network processes autonomously categorize devices and apply appropriate optimization parameters, reducing the perceived complexity for operators while maintaining high optimization precision through automated, intelligent decision-making.
3Adaptability or versatility
If MTC devices are included in general network optimization, then the network capacity to handle diverse devices is improved, but the optimization quality deteriorates because MTC devices skew the optimization results and detrimentally affect mobile broadband user performance
Solution Approach 1:
The patent applies segmentation by separating optimization processes into distinct categories. MTC devices are identified through usage category determination based on device identifiers and behavior patterns, then assigned to specific optimization processes tailored to MTC requirements. This prevents MTC devices from skewing overall network optimization parameters, ensuring that mobile broadband user performance is not detrimentally affected while maintaining the network's ability to handle diverse device types.
Data Source
AI summary
A computer device may include a memory configured to store instructions and a processor configured to execute the instructions to select a base station; obtain one or more metric values for user equipment (UE) devices attached to the selected base station; and determine usage categories for at least some of the UE devices attached to the selected base station, wherein a usage category identifies a combination of a data type, a movement type, and a user type associated with a particular UE device. The processor may be further configured to execute the instructions to classify the obtained one or more metric values based on the determined usage categories; select one or more optimization actions for the selected base station based on the classified one or more metric values; and instruct the selected base station to perform the selected one or more optimization actions.