AI-Based CSI Capability Reporting for Latency-Aware Processing
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Solution Overview
Problem
Existing communication systems face challenges in determining the AI-based Channel State Information (CSI) processing capability of terminals, which is crucial for efficient CSI feedback and compression, especially in 5G New Radio systems.
Innovation Solution
A method and apparatus for determining AI-based CSI processing capability by receiving and analyzing a reported static capability of a terminal, including hardware information, support for AI processing platforms and models, to configure appropriate CSI processing modes and latencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI-based CSI processing is implemented, then CSI feedback accuracy and compression efficiency are improved, but terminal capability determination complexity increases
Solution Approach 1:
The terminal reports its AI-based static capability in advance before actual CSI processing is needed. This preliminary capability declaration allows the network device to pre-determine the processing mode and latency requirements without needing to perform complex real-time capability assessment, thus reducing determination complexity while maintaining high CSI feedback accuracy
Solution Approach 2:
A capability determination mechanism is introduced as an intermediary between the terminal and network device. This mechanism uses reported static capability information as input to determine the appropriate AI-based CSI processing mode, simplifying the overall system complexity while enabling accurate CSI processing
2Productivity
If AI-based CSI compression is used, then transmission efficiency is improved, but processing latency increases
Solution Approach 1:
The system dynamically selects between different CSI processing modes (AI-based compression mode and traditional mode) based on latency requirements and transmission efficiency needs. The network device can flexibly switch between modes to balance compression efficiency and processing latency according to actual communication conditions
Solution Approach 2:
The system changes the processing parameter (mode selection) based on latency requirements. When low latency is required, the system switches to traditional CSI processing mode; when transmission efficiency is prioritized, AI-based compression mode is activated. This parameter change allows optimization of the trade-off between compression efficiency and latency
3Adaptability or versatility
If multiple CSI processing modes are supported, then system adaptability is improved, but device complexity increases
Solution Approach 1:
The CSI processing functionality is segmented into distinct modes (AI-based compression mode and traditional mode), each with specific characteristics. The terminal and network device independently determine and signal the selected mode, simplifying the switching mechanism while maintaining system adaptability to different communication scenarios
Solution Approach 2:
The terminal reports its AI-based static capability as feedback to the network device, which then determines the appropriate processing mode. This feedback mechanism enables automatic mode selection without complex manual configuration, reducing switching complexity while improving system adaptability
Data Source
AI summary
A method for determining an AI-based CSI processing capability is provided. The method includes: receiving a reported AI-based static capability of a terminal; and determining an AI-based CSI processing capability of the terminal on the basis of the static capability.