Wireless Terminal Neural Network Feedback Codebook Segmentation
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Solution Overview
Problem
The integration of artificial intelligence (AI) in wireless communication systems poses challenges in efficiently processing and transmitting feedback information between terminals and base stations, particularly in determining optimal communication configurations due to the complexity introduced by AI functions such as neural networks.
Innovation Solution
The proposed solution involves a method where terminals input downlink channels into their neural networks, process feedback information, and transmit it to base stations using neural network-based codebooks, while base stations receive this information to determine transmission configurations, enabling intelligent and efficient communication by utilizing neural networks for channel estimation and configuration determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If neural networks are deployed in terminals and base stations for AI processing, then communication intelligence and efficiency are improved, but system complexity and processing overhead increase
Solution Approach 1:
The patent segments the neural network processing across multiple entities: terminal devices run local neural networks for channel estimation and feedback generation, while base stations run separate neural networks for configuration determination. This segmentation distributes computational complexity rather than centralizing it, allowing each component to be optimized independently while maintaining overall system intelligence.
Solution Approach 2:
The patent introduces feedback information as an intermediary that carries processed channel data from terminals to base stations. This intermediary structure enables the neural networks at different locations to communicate their processing results without requiring direct complex interaction, simplifying the overall system architecture while maintaining AI-driven intelligence.
2Measurement precision
If feedback information is processed through neural networks with codebooks, then processing precision and configuration accuracy are improved, but processing time and computational load increase
Solution Approach 1:
The patent employs codebooks that are pre-computed and stored, containing optimal transmission configurations for various channel conditions. During actual operation, the neural network only needs to map processed feedback to the nearest codebook entry rather than performing full optimization calculations, significantly reducing processing time while maintaining high accuracy.
Solution Approach 2:
The patent uses simplified neural network architectures and approximate mapping to codebooks that provide sufficiently accurate results with much lower computational cost. Rather than using complex, time-consuming optimization algorithms, the system accepts near-optimal solutions from simpler models, trading minor precision losses for significant speed improvements.
Data Source
AI summary
The present disclosure provides a terminal and a base station in a wireless communication system. The terminal may include a control unit configured to input a downlink channel to a neural network of the terminal; and the control unit further configured to control the neural network of the terminal to process the input and output feedback information.