AI-Based Network CCO Timing for Coverage and Capacity Shifts
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Solution Overview
Problem
Current methods for coverage and capacity optimization (CCO) in wireless communication networks lead to communication disruptions in terminal devices due to delayed adjustments based on current coverage and capacity, failing to account for future changes in network requirements.
Innovation Solution
Predicting future coverage and capacity needs of network devices using artificial intelligence (AI) to determine optimal CCO configurations, and sharing these predictions with adjacent network devices to facilitate timely adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CCO is performed based on current coverage and capacity only after a base station determines that a coverage and capacity problem occurs, then the base station can respond to actual problems, but communication of terminal devices is affected due to delay
Solution Approach 1:
The base station performs prediction on coverage and capacity requirements for a future time period (second time period) based on current status (first time period). The CCO configuration is determined in advance for the future period, and the execution time is set to start at or before the future time period begins. This preliminary action eliminates the delay between problem detection and CCO execution, ensuring communication quality is maintained while avoiding the time loss of reactive approaches.
2Productivity
If CCO configuration is adjusted frequently to meet changing network demands, then network performance is improved, but communication disruptions occur due to timing mismatches
Solution Approach 1:
The system dynamically determines the execution time of CCO configuration based on predicted coverage and capacity requirements for different future time periods. By aligning the execution time with the time period when the predicted requirements actually occur, the system adapts to changing network demands without causing communication disruptions. This dynamic timing adjustment ensures both improved network performance and maintained communication continuity.
Data Source
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AI summary
This application provides a coverage and capacity optimization method and apparatus. In technical solutions provided in this application, first coverage and capacity of a first network device in a second time period may be predicted in a first time period, where the second time period is later than the first time period; a first coverage and capacity optimization CCO configuration of the first network device in the second time period may be determined based on the first coverage and capacity; and then first information may be sent to a second network device, where the first information indicates the first CCO configuration. According to the technical solutions provided in this application, coverage and capacity of a first network device are predicted in advance, and a proper CCO configuration is determined for the first network device, so that communication of a terminal device in the first network device is not affected.