Beamforming Learning Model for Adaptive Antenna Configuration
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Solution Overview
Problem
Existing wireless communication systems face challenges in maintaining optimal radio quality under changing wireless environment conditions, particularly when transmitting Ultra High-Definition (UHD) AV signals, which requires stable and efficient antenna arrangement adjustments.
Innovation Solution
A learning device that uses a processor to generate beamforming learning models based on correlations between antenna configuration factors and communication quality factors, allowing for automatic adjustment of antenna arrangements in response to new wireless environment data sets, thereby optimizing radio quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If traditional wireless communication systems transmit UHD AV signals, then high image quality can be provided, but radio quality deteriorates under changing wireless environment conditions
Solution Approach 1:
The system dynamically adjusts antenna configuration factors based on real-time wireless environment conditions. The learning model continuously adapts antenna arrangements in response to changing environments, transforming the static antenna configuration into a dynamic, environment-aware system that maintains optimal radio quality while supporting UHD transmission
Solution Approach 2:
The system implements a feedback mechanism where communication quality factors are fed back into the learning model to refine antenna configuration decisions. This closed-loop feedback allows the system to learn from actual transmission performance and adjust future antenna arrangements to optimize radio quality under varying conditions
2Reliability
If antenna arrangement is manually adjusted, then radio quality can be optimized for specific conditions, but adaptability to changing wireless environments deteriorates
Solution Approach 1:
The learning model enables the system to self-adjust antenna configurations without manual intervention. By automatically learning from wireless environment data and communication quality feedback, the system performs self-optimization of antenna arrangements, transforming manual adjustment into autonomous adaptation that maintains high radio quality across changing environments
Solution Approach 2:
The system automatically changes antenna configuration parameters based on learned patterns from wireless environment data. The learning model identifies optimal parameter values for different environmental conditions and applies them dynamically, enabling the system to adapt to changing environments through automated parameter adjustment rather than manual reconfiguration
Data Source
AI summary
According to an embodiment of the present disclosure, a learning device may include a communication unit, database configured to store wireless environment data sets representing wireless environments between an image transmission device and an image reception device, and a processor configured to generate a plurality of wireless environment space types respectively mapped to the wireless environment data sets, generate a beamforming learning model by learning a correlation between antenna configuration factors and communication quality factors for each of the wireless environment space types, determine a wireless environment space type corresponding to the new wireless environment data set from among the plurality of wireless environment space types when a new wireless environment data set is received, and determine values of the antenna configuration factors using a beamforming learning model corresponding to the determined wireless environment space type.