Zone-Trained AI Receiver Models for Wireless Signal Handover
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing data transmission and reception, particularly in cell selection, reselection, and handover processes, due to the lack of effective AI-based receiver technologies that adapt to the state of user equipment.
Innovation Solution
A method and apparatus are proposed to enhance data transmission and reception using AI receivers trained based on zones of base stations, involving machine learning to combine receiver models from adjacent base stations, and applying these models for improved cell selection, reselection, and handover processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional receiver technologies are used in wireless communication systems, then the system structure remains simple and easy to implement, but the data transmission efficiency and adaptability to user equipment state are insufficient
Solution Approach 1:
The AI receiver performs self-learning and adaptation by automatically analyzing channel conditions and user equipment states to optimize reception parameters, eliminating the need for complex manual configuration and enabling the system to serve itself in dynamic wireless environments
Solution Approach 2:
The receiver dynamically adjusts its operating parameters based on AI-driven analysis of channel conditions and user equipment state, changing reception characteristics in real-time to optimize performance without requiring complex structural modifications
2Reliability
If AI-based receiver models are trained for each individual base station, then the receiver can adapt to specific base station characteristics, but the training time and system complexity increase significantly
Solution Approach 1:
Multiple base stations are grouped into zones with similar channel characteristics, and a single AI receiver model is trained to serve all base stations within each zone, combining the training efforts and reducing overall training time while maintaining adaptation accuracy through zone-specific modeling
Solution Approach 2:
The AI receiver model is designed with multi-functionality to handle multiple base stations within a zone using a single trained model, allowing the same model to adapt to various base station characteristics through zone-based generalization rather than requiring individual models for each base station
3Stability of the object's composition
If base stations are grouped into zones with similar channel environments, then the AI receiver training efficiency improves and data transmission is stabilized, but the zoning complexity and initial configuration requirements increase
Solution Approach 1:
The system applies local quality by creating zones with homogeneous channel characteristics, allowing the AI receiver to be optimized for specific local conditions while maintaining overall system stability through consistent zone-based approaches across different geographic areas
4Measurement precision
If machine learning is used to combine receiver models from adjacent base stations, then the data reception performance is improved, but the computational complexity and processing requirements increase
Solution Approach 1:
The machine learning process focuses on training the AI receiver model with sufficient but not excessive computational resources, using partial action by selecting key training parameters and stopping criteria that achieve adequate reception accuracy without requiring exhaustive computational power
Data Source
AI summary
A terminal in a wireless communication system receives configuration information from a base station, and receives data on the basis of the configuration information. The configuration information includes information about a first receiver model and a first zone to which the base station belongs, the first zone includes one or more base stations adjacent to the base station, and the first receiver model is generated on the basis of a combination of receiver models that are trained by the base stations within the first zone, according to a machine learning step.