CSI Capability Reporting for Differentiated CPU Allocation
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Solution Overview
Problem
Existing technologies face challenges in determining the processing capability of terminals for AI-based CSI computations, leading to inconsistent and inefficient CSI processing unit computations between terminals and networks.
Innovation Solution
A computation method and apparatus that enable terminals to report their processing capabilities for different types of CSI computations, allowing networks to allocate CPU resources accordingly, based on specific CSI report types and configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If terminals are equipped with specific AI algorithms for CSI computation, then CSI processing capability is improved, but device complexity increases
Solution Approach 1:
The patent segments CSI computation into multiple types (first type and second type) with different computational requirements. Terminals can selectively support different types based on their capabilities, allowing the system to divide the overall CSI processing task into manageable parts that match terminal capabilities.
Solution Approach 2:
The patent introduces dynamic capability reporting where terminals can indicate their supported CSI computation types and quantities through capability information. This allows the system to adaptively allocate CSI computation tasks based on real-time terminal capabilities, making the system flexible rather than static.
2Measurement precision
If terminals report detailed capability information for differentiated CPU computation, then resource allocation accuracy is improved, but information overhead increases
Solution Approach 1:
The patent extracts only the essential capability information needed for CSI computation (supported types and quantities) from the full terminal capability set. This selective extraction provides sufficient information for differentiated resource allocation without requiring complete terminal capability details.
Solution Approach 2:
The patent changes the reporting parameter from comprehensive terminal capabilities to specific CSI computation capabilities (first type and second type quantities). This parameter transformation reduces information overhead while maintaining the precision needed for differentiated CPU computation allocation.
3Productivity
If AI-based CSI computation is implemented, then processing efficiency is improved, but energy consumption increases
Solution Approach 1:
The patent applies local quality by allowing different terminals to have different AI algorithm configurations based on their specific capabilities. Terminals with higher energy constraints can support fewer or simpler CSI computation types, while terminals with more energy availability can support more complex computations, optimizing the energy-efficiency trade-off locally at each terminal.
Data Source
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AI summary
This application provides a method for calculating a channel state information (CSI) processing unit, a device, a terminal, and a network device. The method includes: transmitting, by a terminal, first information and/or second information to a network device; wherein the first information comprises a number of first-type CSI computations that the terminal is capable of supporting simultaneously, and a CSI report of the first-type CSI computation is a first-type CSI report; and the second information comprises a number of second-type CSI computations that the terminal is capable of supporting simultaneously, and a CSI report of the second-type CSI computation comprises the first-type CSI report and a second-type CSI report. By using this method, the terminal can calculate CPU in an adaptive manner according to AI processing capability, thereby introducing a differentiated CPU computing mechanism for AI.