Single AI Model for Variable-Length CSI Feedback
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Solution Overview
Problem
In AI-based channel state information (CSI) compression and feedback solutions, the need for multiple encoding and decoding networks to match different CSI lengths leads to increased power consumption and complexity, as each length of encoded information corresponds to a specific AI network model, requiring extensive training and configuration.
Innovation Solution
A method where a terminal generates and reports target channel feature information based on indication information, using a single AI network model, allowing flexible truncation or selection to achieve different lengths without configuring multiple AI network models, thereby reducing power consumption and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple encoding and decoding networks are used to match different CSI lengths, then the adaptability to different channel state information lengths is improved, but the device complexity and power consumption increase
Solution Approach 1:
The patent applies universality by designing a single AI network model that can process channel state information of different lengths through dynamic padding and truncation mechanisms. The encoder and decoder networks are configured to handle variable-length inputs by adjusting their processing accordingly, eliminating the need for multiple specialized networks for different CSI lengths while maintaining adaptability.
Solution Approach 2:
The patent changes the parameter of the AI network model from being fixed to variable, allowing the model to dynamically adjust its processing based on the actual CSI length. This is achieved through configurable padding and truncation operations that modify the input data to match the expected processing requirements, enabling one model to serve multiple length requirements.
2Measurement precision
If multiple encoding and decoding networks are trained for different CSI lengths, then the measurement precision for different channel states is improved, but the loss of energy increases due to extensive training requirements
Solution Approach 1:
The patent eliminates the need for training multiple separate networks by creating a universal training framework where a single encoder-decoder pair is trained to handle various CSI lengths. The training process incorporates diverse length scenarios, enabling the single model to achieve precise measurement across different channel states without the energy cost of training multiple specialized networks.
Solution Approach 2:
The patent merges the functionality of multiple length-specific networks into a single unified network model. By combining the training objectives and processing capabilities into one model, the system achieves the measurement precision of multiple specialized networks while significantly reducing the energy consumption associated with training and maintaining separate models for each CSI length.
3Manufacturing precision
If compressed CSI with different lengths corresponds to different encoding and decoding networks, then the manufacturing precision of the compression system is improved, but the ease of manufacture deteriorates due to extensive configuration requirements
Solution Approach 1:
The patent simplifies the manufacturing and configuration process by replacing multiple length-specific network configurations with a single universal configuration. The encoder and decoder are designed with inherent flexibility to handle different CSI lengths through dynamic padding and truncation, eliminating the complex configuration requirements of managing multiple networks while maintaining compression precision.
Solution Approach 2:
The patent extracts the length-specific processing requirements from the network configuration and handles them separately through padding and truncation operations. This separation allows the core AI network to maintain a single, simple configuration while still achieving precise compression for different CSI lengths, significantly improving ease of manufacture and deployment.
Data Source
AI summary
This application discloses a channel feature information transmission method and apparatus, a terminal, and a network-side device. The channel feature information transmission method in embodiments of this application includes: receiving, by a terminal, first indication information, and generating first channel feature information based on a first artificial intelligence AI network model; determining, by the terminal, target channel feature information based on the first indication information and the first channel feature information, where the first channel feature information includes the target channel feature information; and reporting, by the terminal, the target channel feature information.