AI CSI Feedback Quantization for Lower Overhead Transmission
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Solution Overview
Problem
Current communication systems face challenges in maintaining feedback information precision while reducing overheads due to the use of fixed projection coordinate systems in channel information feedback, leading to increased data requirements and inefficiencies.
Innovation Solution
Employing an AI network architecture that allows for flexible measurement configurations and feedback reporting by associating branch networks with channel state information, enabling precise quantization and parallel processing to reduce feedback overheads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed projection coordinate system is used for channel information feedback, then the feedback mechanism is simple to implement, but information precision is lost and feedback overhead increases
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed projection coordinate system to a dynamic AI-based feedback mechanism. The terminal device uses trained AI models (such as autoencoders, neural networks, or transformer models) to adaptively process channel state information (CSI) and generate feedback based on learned patterns and characteristics of the channel, rather than relying on predetermined fixed projections.
Solution Approach 2:
The patent changes parameters by using AI models to transform the feedback process. Instead of fixed projection dimensions and coordinate systems, the system uses learnable parameters within neural networks that can be trained to optimize feedback precision. The AI models can dynamically adjust how channel information is compressed and represented, changing the effective parameters of the feedback mechanism based on channel conditions.
2Measurement precision
If spatial-domain dimension, frequency-domain dimension, and measurement-required precision increase, then measurement precision improves, but the amount of feedback information increases
Solution Approach 1:
The patent extracts essential channel information features using AI models. Instead of transmitting complete high-dimensional channel matrices, the terminal device uses trained neural networks to extract the most important channel characteristics and represent them in a compressed form. The AI model identifies and extracts key features that capture the essential channel behavior, discarding redundant information.
Solution Approach 2:
The patent segments the channel information processing into distinct AI-based stages: encoding at the terminal device using trained models, compression through learned representations, and decoding at the network device. This segmentation allows each component to be optimized independently, with AI models handling the complex transformation and compression tasks.
3Reliability
If traditional projection-based feedback is used, then implementation is straightforward, but feedback overhead increases with higher precision requirements
Solution Approach 1:
The patent applies preliminary action by pre-training AI models offline before actual channel feedback operation. The terminal device and network device perform extensive training sessions using historical channel data to teach the neural networks how to effectively compress and reconstruct channel information. This preliminary training phase allows the models to learn optimal compression strategies and reconstruction methods, so that during actual operation, the feedback process is efficient and accurate without requiring real-time complex computations.
Data Source
AI summary
This application provides an information transmission method and apparatus. The method includes: receiving first indication information from a network device, where the first indication information indicates an association relationship between a plurality of branch networks in an artificial intelligence AI network and channel state information CSI measurement configuration information; and obtaining first quantization information based on a first branch network and channel information, where the first branch network is associated with current CSI measurement configuration information, and the first branch network belongs to the plurality of branch networks in the AI network.