AI-Based Channel State Information Reporting Framework
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Solution Overview
Problem
Current wireless communication systems face challenges in accurately reporting channel state information (CSI) while minimizing power consumption, especially as devices become more feature-rich and complex, leading to strain on battery life without compromising transmit and receive abilities.
Innovation Solution
An artificial intelligence-based framework for CSI reporting is introduced, allowing for network-controlled or device-controlled AI model training and deployment, enabling efficient CSI reporting through signal exchange and negotiation between wireless devices and cellular networks, using specific AI models for improved accuracy and reduced power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional channel state information reporting methods are used, then device functionality and communication ability are maintained, but power consumption increases significantly
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods with AI-based neural networks to perform CSI reporting. The neural network model processes channel state information through learned transformations, achieving accurate CSI feedback with significantly reduced computational complexity and power consumption compared to conventional signal processing approaches.
Solution Approach 2:
The patent changes the operational parameters of CSI reporting by introducing AI model training configurations, including training data characteristics, model architecture parameters, and inference settings. These parameter optimizations enable the system to achieve accurate CSI reporting while controlling power consumption through efficient model execution.
2Use of energy by moving object
If AI-based CSI reporting is implemented, then power consumption is reduced, but system complexity increases
Solution Approach 1:
The patent segments the AI-based CSI reporting system into distinct components: training phase components (data collection, model training) and inference phase components (model deployment, execution). This segmentation allows the complex system to be managed through modular stages, where the training phase prepares AI models offline and the inference phase executes them with controlled complexity.
Solution Approach 2:
The patent introduces AI models as intermediary components between the wireless communication system and the CSI reporting function. These models act as mediators that process channel state information through learned transformations, simplifying the overall system architecture by encapsulating complex processing logic within the AI model rather than implementing it through complex traditional signal processing circuits.
3Measurement precision
If AI models are trained and deployed for CSI reporting, then CSI accuracy is improved, but training and deployment complexity increases
Solution Approach 1:
The patent applies preliminary action by performing AI model training in advance during a training phase before actual CSI reporting is needed. Training data is collected and models are trained offline, preparing the system in advance. During the inference phase, only the pre-trained models need to be deployed and executed, significantly simplifying the overall process compared to training models in real-time.
Solution Approach 2:
The patent implements feedback mechanisms where CSI reporting results are used to evaluate and refine AI models. The system receives feedback from CSI measurements and can use this information to validate model performance and adjust training processes, creating a closed-loop system that improves accuracy while providing guidance for easier model development and deployment.
Data Source
AI summary
This disclosure relates to techniques for providing an artificial intelligence based framework for performing channel state information reporting in a wireless communication system. A cellular base station may provide system information for a cell to a wireless device. The system information may indicate that the cell supports artificial intelligence based channel state information reporting. The wireless device may provide wireless device capability information to the cellular base station. The capability information may indicate that the wireless device supports artificial intelligence based channel state information reporting. The wireless device may determine an artificial intelligence model to use to perform channel state information reporting with the cell, and may perform channel state information reporting using the selected artificial intelligence model.