Sequential CSI Training for Shared UE Encoder Compatibility
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Solution Overview
Problem
Existing wireless communication networks face interference issues due to increased demand for mobile broadband access, leading to degraded performance, and existing cross-node training methods for channel state feedback reveal proprietary information, requiring separate encoders and decoders for each device combination.
Innovation Solution
Implement UE-driven sequential training to generate a shared encoder and decoder using a sequential training dataset, allowing multiple devices to use a universal decoder without revealing proprietary designs, by training a decoder based on channel state information data and transmitting a sequential training dataset to a network node.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cross-node training methods are used for channel state feedback, then encoder and decoder compatibility is improved, but proprietary information is revealed
Solution Approach 1:
The patent introduces a third-party training data generator that acts as an intermediary between different device vendors. This intermediary collects channel state information from multiple nodes and generates training datasets without requiring direct sharing of proprietary encoder/decoder models. The training data includes encoded feedback and corresponding channel state information, enabling cross-node training while protecting vendor-specific proprietary information through indirect data sharing rather than direct model exchange.
2Loss of information
If separate encoders and decoders are used for each device combination, then proprietary information is protected, but device complexity increases
Solution Approach 1:
The patent implements universal encoders and decoders that can function across multiple device combinations and vendor platforms. Instead of requiring separate encoders for each device pair, the system trains a single universal encoder that processes channel state feedback from various nodes and a universal decoder that reconstructs the feedback for different receiving devices. This multi-functional approach maintains proprietary information protection through vendor-specific training data while reducing overall system complexity by sharing common encoding/decoding infrastructure.
3Productivity
If more UEs access wireless networks, then mobile broadband demand is met, but interference increases and performance degrades
Solution Approach 1:
The patent implements a feedback mechanism where UEs transmit channel state information about their received signals back to the network. This feedback data is then used to train encoders and decoders that can better compensate for interference conditions. By continuously monitoring and feeding back channel quality information, the system adapts its encoding/decoding parameters to optimize performance under varying interference levels, allowing more UEs to access the network with improved signal quality despite increased interference.
Data Source
AI summary
This disclosure provides systems, methods, and devices for wireless communication that support UE-driven sequential training. In a first aspect, a method of wireless communication includes obtaining channel state information data associated with a second network node; training a shared UE encoder based on the channel state information data and based on a decoder to generate a sequential training dataset; and transmitting the sequential training dataset to a third network node. Other aspects and features are also claimed and described.