Wireless Signal Reception Using Channel-Specific DNN Selection
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Solution Overview
Problem
Existing deep learning-based wireless communication systems face challenges in efficiently adapting to dynamic and diverse wireless channel environments due to limitations in training data and the mismatch between static training data and dynamic channel characteristics.
Innovation Solution
A method and apparatus that utilize pre-trained deep neural networks (DNNs) for specific target channel environments, selecting DNN candidates based on adjacent environments with minimal loss, and combining them to effectively transmit and receive signals in wireless communication systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep neural networks are pre-trained for specific channel environments, then signal transmission performance is improved, but device complexity increases due to managing multiple DNN models for different environments
Solution Approach 1:
The patent segments the channel environment into multiple distinct types (e.g., urban, suburban, rural) and trains separate DNN models for each segment. This allows the system to specialize models for specific environments, improving performance while managing complexity through organized categorization rather than treating all environments uniformly.
Solution Approach 2:
The patent changes the parameter of environment-specific adaptation by training DNNs with different configuration parameters tailored to specific channel conditions. Each DNN model has parameters optimized for its target environment, allowing the system to adapt to varying channel characteristics without requiring a completely different model architecture.
2Adaptability or versatility
If multiple DNN models are trained for different channel environments, then adaptability to diverse environments is improved, but loss of time increases due to DNN selection and combining overhead
Solution Approach 1:
The patent performs preliminary classification of the current channel environment into predefined categories before DNN selection. This pre-categorization allows the system to quickly identify which pre-trained DNN models are relevant, avoiding exhaustive search and reducing the time required for model selection and combining operations.
Solution Approach 2:
The patent introduces an environment classification mechanism as an intermediary step between channel observation and DNN selection. This intermediary categorizes the channel conditions and maps them to appropriate DNN models, streamlining the selection process and reducing decision-making time.
3Manufacturing precision
If the entire target channel environment is divided into many sub-environments, then training precision is improved, but device complexity increases due to larger number of DNN candidates
Solution Approach 1:
The patent applies local quality by creating specialized DNN models for specific local channel conditions (e.g., urban canyons, rural open areas) rather than using a single general-purpose model. Each local environment receives tailored training data and model parameters, improving precision for that specific condition while maintaining manageable model counts through focused specialization.
Data Source
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AI summary
An embodiment relates to a next-generation wireless communication system for supporting higher data transmission rates or the like after the 4th generation (4G) wireless communication system. According to an embodiment, a method for transmitting and receiving signals in a wireless communication system and a device supporting same may be provided, and another embodiment may be provided.