AI/ML-Based CSI Reporting With Priority Rules for Overlap Resolution
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently determining channel state information (CSI) reports due to overlapping CSI reports on PUSCH or PUCCH, data collection, and model monitoring information, leading to increased system overhead and reduced communication performance and reliability.
Innovation Solution
Implementing AI/ML-based methods for determining CSI reports that include auto-encoder outputs, compression ratios, quantization levels, rank indicators, and ML model monitoring outcomes, along with priority rules to manage overlapping reports and reduce system overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple CSI reports are transmitted on PUSCH or PUCCH, then CSI feedback information is provided, but overlapping occurs leading to increased system overhead and reduced reliability
Solution Approach 1:
The patent segments CSI reports into different types (first type and second type) with different priority levels. The first type contains critical CSI information while the second type contains less critical information. This segmentation allows the system to prioritize transmission of essential CSI data, resolving the contradiction by ensuring reliable delivery of critical information while managing overall system overhead through selective transmission.
Solution Approach 2:
The patent introduces priority parameters to differentiate between various CSI report types. By assigning different priority levels to different CSI reports, the system can dynamically adjust transmission behavior based on the importance of the information, thereby maintaining reliability for critical reports while controlling system overhead through parameter-based differentiation.
2Productivity
If AI/ML based CSI feedback is implemented, then communication performance is enhanced, but determining priority rules for overlapping reports becomes complex
Solution Approach 1:
The patent establishes priority rules in advance before CSI report transmission occurs. The network device pre-configures which CSI report types have higher or lower priorities. When overlapping occurs, these pre-established rules automatically determine the transmission order without requiring complex real-time decision-making, thus enhancing communication performance while avoiding complexity in priority determination.
Solution Approach 2:
The system uses self-service mechanisms where the priority rules automatically resolve overlapping CSI reports without requiring external intervention or complex algorithms. The pre-configured priority system enables the network to autonomously manage report transmission conflicts, maintaining high communication performance while keeping the priority determination process simple and efficient.
3Measurement precision
If data collection and model monitoring information are included in CSI reports, then AI/ML model performance is improved, but overlapping with CSI reports increases system overhead
Solution Approach 1:
The patent merges AI/ML model monitoring information with CSI reports into a unified transmission framework. By combining these different types of information into the same reporting mechanism with integrated priority rules, the system improves measurement precision for model monitoring while avoiding the overhead of separate transmission channels. The merging allows efficient resource utilization and simplified management of multiple information types.
Data Source
AI summary
Communication devices and methods for determining channel state information (CSI) report based on artificial intelligence (AI)/machine learning (ML) are provided. The method for determining CSI report based on AI/MI performed by a communication device includes determining, by the communication device, one or more CSI reports according to an AI/ML based CSI feedback, wherein each of the one or more CSI reports contains an output of an auto-encoder, a compression ratio, a rank indicator, quantization levels, a ground truth of an enhanced CSI feedback, and/or an ML model monitoring outcome, and determining, by the communication device, priority rules for the CSI reports according to the AI/ML based CSI feedback.