Base Station Cell Deployment Data for AI Network Prediction Precision
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Solution Overview
Problem
Modern communication networks, such as 5G networks, face challenges in managing a large number of user equipments and varying user behaviors due to complex network situations, leading to inefficiencies in data collection and analysis, which affects the precision and relevance of network predictions and statistics.
Innovation Solution
A method is proposed where base stations in a cellular network provide current deployment conditions of cells to data collection and analysis devices, using an application programming interface, to enhance the input data for AI models and improve the precision and relevance of network predictions and statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If base stations send current deployment conditions information to data collection and analysis devices, then the precision and relevance of network predictions and statistics are improved, but the device complexity and data processing load increase
Solution Approach 1:
The patent applies preliminary action by having base stations pre-collect and store current deployment conditions information (such as cell deployment status, neighboring cell relationships, and infrastructure details) before the data collection and analysis device requests it. This preparation in advance reduces the processing burden during actual data collection, as the information is already organized and ready for transmission, thereby improving prediction precision without proportionally increasing device complexity.
2Measurement precision
If base stations send current deployment conditions information to data collection and analysis devices, then the relevance of network statistics is improved, but the loss of time for data collection and transmission increases
Solution Approach 1:
The patent applies the extraction principle by selectively transmitting only the most relevant current deployment conditions information from base stations to data collection and analysis devices. Instead of sending all possible data, the system extracts and transmits specifically the information that directly impacts prediction relevance (such as deployment conditions affecting user behavior patterns), thereby improving statistic relevance while minimizing data transmission time.
3Reliability
If base stations send current deployment conditions information to data collection and analysis devices, then the quality of decisions in the core network is improved, but the use of energy for data transmission and processing increases
Solution Approach 1:
The patent applies local quality by having base stations transmit current deployment conditions information selectively based on local network conditions and the specific analytical needs of the data collection device. Rather than universal continuous transmission, the system adapts the data transmission to local requirements - sending detailed deployment information only when and where it is needed for decision-making, thereby improving core network decision quality while reducing overall energy consumption for data transmission.
Data Source
AI summary
A method for providing information by a base station of a cellular communications network is provided. The method includes, following a request to a network data collection and analysis device, transmitting, to the collection and analysis device, information representative of current deployment conditions of at least one network cell managed by the base station.