AI Physical Channel Modeler for Base Station Testing
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Solution Overview
Problem
Existing methods for testing base station performance in physical fields are inefficient, either wasting resources with numerous terminal devices or providing inaccurate simulations due to environmental changes, and existing channel modeling assumes ideal conditions, leading to outdated or inaccurate results.
Innovation Solution
A system utilizing a physical channel modeler and training module with AI algorithms, including a generator and discriminator, to infer fully real physical field channel features from sparse measurements and geographic information, allowing for accurate simulation with fewer measurement points and adaptive to environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of terminal devices are used to perform measurement, then fully real physical field channel feature can be obtained, but measurement devices and manpower are wasted
Solution Approach 1:
The patent segments the measurement task by using a small number of terminal devices to collect channel features at different geographic locations, then processes these segmented measurements through an AI model to reconstruct the complete physical field channel characteristics, avoiding the need for dense device deployment
Solution Approach 2:
The patent introduces an AI inference model as an intermediary between sparse measurements and complete channel features. The model learns the mapping relationship from limited measurement data and generates comprehensive physical field channel features without requiring actual dense measurements
2Measurement precision
If a single device performs point-by-point measurement continuously, then fully real physical field channel feature can be obtained, but measurement time and manpower are wasted
Solution Approach 1:
The patent performs preliminary action by training the AI inference model offline using historical measurement data. Once trained, the model can rapidly infer physical field channel features for new scenarios without requiring time-consuming continuous measurements, achieving both accuracy and speed
3Ease of manufacture
If physical channel modeler builds channel model under ideal state assumption, then simulated physical field channel features can be obtained, but simulation accuracy is significantly inaccurate due to environmental imperfections
Solution Approach 1:
The patent changes the parameters used in channel modeling from idealized theoretical parameters to real-world parameters learned from actual measurements. The AI model learns the relationship between geographic information and actual channel features, adapting to environmental imperfections by using empirical data rather than theoretical assumptions
Data Source
AI summary
A system for synthesizing signal of user equipment and a method thereof are provided. The system includes a physical channel modeler and a physical channel training module. The physical channel modeler receives geo information of a field under test of and a sparse real physical field channel feature to build a physical channel model. The physical channel modeler estimates a plurality of predefined positions of the geo information to obtain a plurality of simulated physical field channel features corresponding to the predefined positions. The physical channel training module receives and performs training on the geo information, the sparse real physical field channel feature and the simulated physical field channel features by using an AI algorithm to perform an inference of a fully real physical field channel feature.