Incremental Weight Neural Network for Wireless Feedback
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Solution Overview
Problem
Current wireless communication systems face challenges in providing efficient feedback mechanisms, particularly in learning and adapting weights for data transmission and retransmission processes using neural networks.
Innovation Solution
A method and apparatus for user equipment (UE) in a wireless communication system that learns and applies incremental weights through an artificial neural network for data transmission and retransmission, with the ability to puncture weights and share information for improved feedback efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional feedback mechanisms are used in wireless communication systems, then system simplicity is maintained, but feedback efficiency and adaptability are insufficient
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting neural network weights based on transmission conditions. The weights are modified incrementally through learning processes, allowing the system to adapt feedback mechanisms to varying channel conditions and transmission requirements, thereby improving feedback efficiency without requiring complete system redesign
Solution Approach 2:
The patent implements preliminary action by pre-learning weights during idle periods or using pilot signals before actual data transmission. This allows the neural network to prepare optimal weight configurations in advance, enabling faster and more efficient feedback processing when actual transmission occurs, thus improving productivity without proportionally increasing operational complexity
2Productivity
If neural network weights are learned and applied for data transmission, then transmission efficiency is improved, but learning time and computational resources increase
Solution Approach 1:
The patent applies partial action by updating only specific weight parameters that are most relevant to current transmission conditions rather than relearning the entire neural network. This incremental weight update approach maintains transmission efficiency while significantly reducing the time and computational resources required compared to complete relearning
Solution Approach 2:
The patent implements periodic action by performing weight learning and updates at specific intervals or triggered by certain events (e.g., channel condition changes, transmission errors). This periodic learning strategy balances the need for adaptive transmission efficiency with the constraint of limited learning time, allowing the system to maintain optimal performance without continuous heavy computational overhead
3Adaptability or versatility
If incremental weight scheme is used for retransmission, then feedback adaptability is improved, but weight management complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the weight management process into distinct phases: initial weight learning, incremental weight updates based on feedback, and weight application during transmission and retransmission. This segmentation allows each phase to be optimized independently, improving feedback adaptability while making weight management more systematic and less complex
Data Source
AI summary
The present disclosure a method of transmitting data by user equipment (UE) in a wireless communication system, the method comprising: transmitting data applied a first transmission weight learned through an artificial neural network, to a base station, based on the UE performing data transmission, receiving NACK related to the data transmission from the base station, and retransmitting data applied a second transmission weight learned through the artificial neural network, to the base station, based on the UE performing retransmission of the data, wherein the first transmission weight and the second transmission weight are learned based on an incremental weight (IW) scheme, and wherein the second transmission weight is an additional weight that is learned by the artificial neural network based on the IW scheme with the first transmission weight being fixed.