Multi-Device Neural Network Training for Wireless Channel Feedback
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Solution Overview
Problem
In wireless communication systems, training overheads are significant due to the need for a base station to separately train with multiple user equipment devices, leading to inefficiencies in channel feedback and intelligent decision-making processes.
Innovation Solution
A communication method involving a second device that receives policy-related information from multiple first devices, updates a neural network based on reward information, and shares update parameters with these devices, reducing the need for individual training sessions by aggregating updates from all devices simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a base station separately trains with each user equipment device, then individual training accuracy is improved, but training overhead increases significantly
Solution Approach 1:
The patent combines multiple individual training processes into a single unified training session. The base station aggregates channel state information from multiple user equipment devices and performs joint training of neural networks, thereby reducing repeated training overhead while maintaining individualized accuracy through shared model parameters and device-specific input features.
Solution Approach 2:
The patent creates a universal training framework where a single training process serves multiple user equipment devices simultaneously. The neural network model is designed to handle multiple devices with different channel characteristics through a unified architecture that processes device-specific inputs while sharing common computational layers, achieving multi-functionality in the training system.
2Adaptability or versatility
If separate training sessions are conducted for each device, then device-specific optimization is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple device-specific training processes into a single unified training operation. By combining channel state information from multiple devices and performing joint neural network training, the system reduces the number of separate training sessions while maintaining device-specific optimization through shared model parameters that adapt to individual device characteristics.
Solution Approach 2:
The patent segments the training process into device-specific input processing and shared model training components. Each user equipment device provides its own channel state information as input, while the neural network model shares common layers and parameters across all devices, achieving a balance between device-specific optimization and system-wide efficiency.
Data Source
AI summary
A communication method is disclosed. The method includes: a second device receives policy related information from M first devices; the second device obtains transmission decisions of the M first devices based on the policy related information by using a second neural network; the second device updates the second neural network based on reward information, and sends, to the M first devices, information for updating a first neural network, and the third device obtains second update parameter information of the first neural network based on the first update parameter information of the first neural network of the M first devices, and sends the second update parameter information of the first neural network to the M first devices, so that the first device may update the first neural network. The second update parameter information is obtained in a training process, so that training overheads can be reduced.