Configurable Neural Network for Channel State Feedback Learning
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Solution Overview
Problem
Current 5G NR and LTE technologies face challenges in optimizing channel state feedback (CSF) for efficient wireless communication, particularly in supporting diverse user equipment (UE) scenarios and base station configurations, which affects spectral efficiency and overall network performance.
Innovation Solution
Implementing a method for user equipment (UE) and base stations to receive and transmit multiple neural network training configurations for channel state feedback (CSF), allowing each UE to train decoder/encoder pairs according to different neural network frameworks, enabling adaptable CSF processing and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple neural network training configurations are implemented for different UE scenarios and base station configurations, then adaptability and spectral efficiency are improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent implements dynamic neural network configurations where the base station and UE can select and switch between multiple pre-trained neural network models based on current channel conditions, traffic patterns, and device capabilities. This allows the system to adapt to varying scenarios without permanently increasing hardware complexity, as only the software configuration changes dynamically.
Solution Approach 2:
The patent changes key parameters of the neural network processing, including different input feature sets, network architecture parameters, and training objectives, to optimize performance for specific scenarios such as high mobility, low latency, or massive connectivity. By parameterizing the neural network configurations, the system achieves versatility without redesigning the underlying processing infrastructure.
2Productivity
If multiple neural network training configurations are implemented for different UE scenarios and base station configurations, then spectral efficiency is improved, but processing time and training overhead increase
Solution Approach 1:
The patent applies preliminary action by pre-training multiple neural network configurations offline before deployment. During actual operation, the base station and UE simply select from these pre-trained models rather than performing extensive real-time training. This approach achieves high spectral efficiency through optimized processing while minimizing the time loss associated with training, as the heavy computational work is completed in advance.
3Productivity
If multiple neural network training configurations are implemented for different UE scenarios and base station configurations, then network performance is improved, but ease of operation and implementation difficulty increase
Solution Approach 1:
The patent creates a universal framework where a single base station and UE implementation can support multiple neural network configurations through standardized interfaces and selection mechanisms. The system provides multi-functionality by handling diverse scenarios (different UEs, base station types, channel conditions) through a unified architecture that automatically selects appropriate pre-trained models, thereby improving network performance without proportionally increasing operational complexity.
Data Source
AI summary
A method of wireless communication, by a user equipment (UE), includes receiving multiple neural network training configurations for channel state feedback (CSF). Each configuration corresponds to a different neural network framework. The method also includes training each of a group of neural network decoder/encoder pairs in accordance with the received training configurations. A method of wireless communication, by a base station, includes transmitting multiple neural network training configurations to a user equipment (UE) for channel state feedback (CSF). Each configuration corresponds to a different neural network framework. The method also includes receiving a neural network decoder/encoder pair trained in accordance with the training configurations.