Cell Network Optimization via Multi-Parameter Security Status
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Solution Overview
Problem
Current SON-based cellular networks rely heavily on manual analysis and empirical methods for network optimization, requiring significant human resources and expert knowledge, and the optimization algorithms triggered by KPIs often fail to accurately reflect network issues, leading to inefficient maintenance and optimization processes.
Innovation Solution
The method involves acquiring and averaging multiple groups of network status parameters and cell control parameters to accurately represent the network's running status, using KPIs and cell measurement parameters to determine the security status of cells, and performing network optimization based on this data, reducing the need for manual intervention and improving algorithm accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and empirical methods are used for network optimization, then network status can be understood through expert knowledge, but human resource costs and time consumption increase significantly
Solution Approach 1:
The system enables self-service through automated network optimization. The network management device automatically collects KPI data, determines network status, selects optimization algorithms, and configures parameters without requiring manual expert intervention. This transforms the manual analysis process into an automated self-service system that reduces both time consumption and human resource requirements while maintaining optimization accuracy
Solution Approach 2:
The patent replaces the mechanical system of manual expert analysis with an automated information processing system. Instead of relying on human experts to manually analyze network status and determine optimization strategies, the system uses automated data collection, processing, and algorithm selection mechanisms that substitute human mechanical operations with electronic automation, thereby reducing time loss and human resource costs
2Device complexity
If only KPI statistics are used to represent network running status, then data collection is simplified, but the ability to accurately reflect network problems decreases
Solution Approach 1:
The patent segments the network status representation into multiple dimensions: KPI statistics (first parameter), cell measurement parameters (second parameter), and optionally security parameters (third parameter). This segmentation allows the system to maintain simplified data collection processes while improving problem detection accuracy by analyzing multiple parameter types together, rather than relying on a single comprehensive but complex data structure
3Ease of operation
If optimization algorithms are triggered only by KPI thresholds, then the triggering mechanism is simple, but the accuracy of identifying actual network issues decreases
Solution Approach 1:
The patent adds another dimension to the algorithm triggering mechanism by incorporating cell measurement parameters and security parameters alongside KPI statistics. Instead of relying solely on the single dimension of KPI threshold crossing, the system evaluates multiple parameter dimensions simultaneously, enabling more accurate identification of actual network issues while maintaining operational simplicity through automated multi-parameter evaluation
4Measurement precision
If multiple network parameters are collected and analyzed, then network status representation accuracy improves, but processing complexity and resource requirements increase
Solution Approach 1:
The patent introduces an intermediary mechanism in the form of a network management device that acts as a mediator between data collection and optimization execution. This intermediary automatically performs parameter collection, processing, analysis, and algorithm selection, thereby improving network status representation accuracy through multi-parameter analysis while hiding the processing complexity from users and reducing the perceived resource requirements through automated efficiency
Data Source
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AI summary
Embodiments of the present invention provide a network optimization method. The method includes: acquiring, in a first time segment, a first network status parameter that is determined according to M groups of network status parameters of at least one target cell, and acquiring a first CP that is determined according to M groups of cell control parameters CP of the target cell, where the network status parameters include a cell statistics key performance parameter KPI and a cell measurement parameter, the KPI is used to indicate running performance of a cell, the cell measurement parameter is used to indicate service distribution of a cell and/or a resource usage status of a cell, the CP is used to indicate base station setting of a cell, and the M groups of CP and the M groups of network status parameters are in a one-to-one correspondence; determining, according to the first network status parameter and from a first entry that records a mapping relationship between a network status parameter and a security status, a security status of the target cell in the first time segment; and performing network optimization on the target cell according to the security status of the target cell in the first time segment.