CSI Report Processing Unit Allocation in Wireless Nodes
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Solution Overview
Problem
The introduction of ML/AI-based CSI report technology in wireless communication systems poses a challenge in determining the appropriate quantity of processing units required for efficient processing, differing significantly from conventional CSI calculations.
Innovation Solution
A method is disclosed where the quantity of type-I processing units occupied by the CSI report is determined based on whether the CSI report configuration is used to generate a specific function, optimizing processing unit allocation according to the requirements of the CSI report.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ML/AI-based CSI report technology is introduced, then processing capability and system efficiency are improved, but determining the appropriate quantity of processing units becomes complex and challenging
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the quantity of processing units allocated to CSI reports based on the report type (conventional vs. ML/AI-based). The system changes the parameter of processing unit quantity from fixed to variable, allowing efficient allocation that adapts to different CSI report requirements, thereby improving processing efficiency while managing complexity.
Solution Approach 2:
The patent segments the CSI report processing into distinct categories (conventional CSI reports and ML/AI-based CSI reports), each with different processing unit requirements. By dividing the processing workload into separate segments with dedicated resource allocation rules, the system simplifies the overall allocation complexity while maintaining high processing efficiency for each segment type.
2Device complexity
If a unified solution is adopted for different CSI report scenarios, then hardware complexity and cost are reduced, but flexibility in optimizing for specific ML/AI requirements may be limited
Solution Approach 1:
The patent implements universality by creating a unified processing framework that handles both conventional and ML/AI-based CSI reports through a common architecture. The system uses a universal resource allocation mechanism that adapts to different report types without requiring separate hardware implementations, thereby reducing hardware complexity while maintaining the ability to optimize for specific scenarios through software-configurable parameters.
Data Source
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AI summary
Disclosed in the present application are a method and an apparatus used in nodes for wireless communication. A first node receives a first channel state information (CSI) report configuration and updates a first CSI report. The first CSI report configuration is used to determine the first CSI report; the first CSI report configuration includes a first higher-layer parameter and a second higher-layer parameter; the first higher-layer parameter is set to a first parameter value, which belongs to a first candidate parameter value subset; the second higher-layer parameter is used to determine a first reference signal (RS) resource group, which is used for channel measurement; the first CSI report does not occupy type-I processing units prior to a first symbol; and a quantity of the type-I processing units occupied by the first CSI report is related to whether the first CSI report configuration is used to generate a first function. The above method meets the requirements for CSI processing capabilities while avoiding the waste of the processing capabilities and optimizing system design.