AI-Based Channel Estimation Timing for Accurate PDSCH Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing communication protocols, such as 3GPP TS 38.214, fail to accurately determine PDSCH processing time (Tproc) in 5G mobile communication systems when artificial intelligence (AI) models are introduced for channel estimation, leading to inefficiencies in processing time configuration.
Innovation Solution
Determine PDSCH processing time based on the processing time of AI models, considering factors like AI complexity, type, and DMRS configuration, to enhance accuracy and flexibility in configuring PDSCH processing time in AI-based channel estimation scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing communication protocols are used to determine PDSCH processing time, then the protocol compatibility is maintained, but the accuracy of processing time determination deteriorates in AI-based channel estimation scenarios
Solution Approach 1:
The patent introduces new parameters including AI model processing time, AI model complexity, and AI model type as additional factors to the existing PDSCH processing time determination. By changing the parameter set to include AI-specific metrics, the system achieves both protocol compatibility and accurate timing in AI-based scenarios.
Solution Approach 2:
The patent segments the processing time into distinct components: traditional PDSCH processing time and AI model processing time. This segmentation allows the system to maintain existing protocol requirements while adding AI-specific processing considerations, thereby improving overall accuracy without losing adaptability.
2Measurement precision
If AI models are introduced for channel estimation, then the channel estimation accuracy is improved, but the PDSCH processing time determination becomes more complex
Solution Approach 1:
The patent introduces an intermediary parameter set that includes AI model processing time, complexity, and type information. These intermediary parameters serve as bridges between the AI channel estimation process and the PDSCH processing time determination, simplifying the overall complexity by providing structured intermediate values.
Solution Approach 2:
The patent replaces complex mechanical/time-based processing time determination with a more flexible parameter-based approach that incorporates AI model characteristics. This substitution allows the system to handle AI-based channel estimation without increasing operational complexity.
3Measurement precision
If processing time is extended to accommodate AI model processing, then the AI-based channel estimation accuracy is improved, but the communication efficiency deteriorates
Solution Approach 1:
The patent makes the processing time determination dynamic by incorporating AI model processing time as a variable factor. The system can adjust the total processing time based on the specific AI model being used, its complexity, and its type, thereby optimizing communication efficiency for each specific scenario rather than using a fixed time allocation.
Data Source
AI summary
A communication method includes: determining, by a network device, processing time of one or more first AI models, wherein the one or more first AI models are one or more neural network models taken by a terminal device for a channel estimation; and determining, by the network device, first PDSCH processing time corresponding to the one or more first AI models based on at least the processing time of the one or more first AI models.