Dual-Connectivity Base Station Selection Using AI/ML Models
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Solution Overview
Problem
Existing mobile communication systems face challenges in optimizing key performance indicators such as delay, reliability, connection density, and energy efficiency due to limited use cases for AI/ML integration, preventing consistent optimization across various communication statuses.
Innovation Solution
A base station that supports dual connectivity, allowing a communication terminal to connect to two base stations, with the master base station using AI/ML to select a secondary base station based on information from connected terminals and neighboring stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mobile communication systems are used without AI/ML integration, then system operation is simple and device complexity is low, but key performance indicators such as delay, reliability, connection density, and energy efficiency cannot be consistently optimized across various communication statuses
Solution Approach 1:
An AI/ML model is introduced as an intermediary component between the master base station and the decision-making process for secondary base station selection. The model receives communication status information as input and outputs optimized decisions, mediating between raw data and control actions to achieve KPI optimization without requiring complex manual configuration
Solution Approach 2:
The AI/ML model is trained in advance using historical communication status data and optimal decision outcomes. This preliminary training phase allows the model to learn optimal strategies for different communication scenarios, so that during actual operation, the model can make immediate optimized decisions without requiring real-time complex computations
2Productivity
If AI/ML is integrated into mobile communication systems, then optimization of key performance indicators is achieved, but device complexity and computational requirements increase
Solution Approach 1:
The AI/ML model undergoes extensive training offline using historical data to learn optimal decision patterns. Once trained, the model can make rapid predictions during actual operation without requiring complex real-time computations, thus achieving high productivity while keeping online computational complexity manageable
Solution Approach 2:
The AI/ML model creates simplified representations or copies of complex communication patterns and decision logic. Instead of implementing complex optimization algorithms directly in the base station, the trained model captures essential patterns in a more compact and efficient form that can be executed with lower computational overhead
Data Source
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AI summary
A base station is a base station of a communication system that supports dual connectivity in which a communication terminal is simultaneously connected to two base stations, in which, when the base station operates as a master base station that is a master node of the dual connectivity, the base station decides another base station that operates as a secondary base station that is a secondary node of the dual connectivity, by using a model that has been trained using information acquired from a communication terminal connected to a serving base station and other neighboring base stations.