Model-Based CSI Reporting for Low-Overhead Multi-Hypothesis Feedback
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Solution Overview
Problem
The increasing complexity of channel state information (CSI) reporting in 5G networks, particularly due to the rise in hypotheses for which CSI reporting is performed, leads to challenges in resource utilization and accuracy, especially in scenarios involving multiple transmission reception points and high-frequency communications, resulting in inefficient use of uplink resources and potential inaccuracies in link adaptation.
Innovation Solution
A model-based approach for CSI reporting where coefficients of a regression or classification model are reported instead of individual CSI quantity values, allowing for more efficient use of uplink resources and reducing overhead by compressing the payload, while enabling accurate determination of CSI quantities across multiple conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If individual CSI quantity values are reported for multiple hypotheses, then comprehensive CSI information is provided, but uplink resource overhead increases significantly
Solution Approach 1:
The patent uses a machine learning model to learn the mapping relationship between CSI quantities under different hypotheses and a small number of reference CSI quantities. Instead of transmitting all individual CSI values, the UE transmits only the model parameters (weights and biases) which can be used to reconstruct any needed CSI quantity, effectively creating a compact representation that preserves information while reducing overhead.
Solution Approach 2:
The patent transforms the representation of CSI information from direct quantity values to model parameters (weights and biases). This parameter transformation allows the system to convey the same information content using fewer bits, as the model parameters can generate multiple CSI quantities through computation rather than requiring explicit transmission of each quantity.
2Quantity of substance
If model-based CSI reporting is implemented, then uplink resource overhead is reduced, but complexity of determining CSI quantities increases
Solution Approach 1:
The patent performs preliminary work by having the gNB configure the ML model parameters (weights and biases) in advance through RRC signaling. This preliminary configuration eliminates the need for complex real-time model training or parameter negotiation during operation, reducing the runtime complexity while maintaining the overhead reduction benefits.
Solution Approach 2:
The patent introduces a pre-configured machine learning model as an intermediary between the transmitted model parameters and the final CSI quantities. This intermediary handles the complex computation of transforming reference CSI quantities into multiple hypothesis-specific CSI quantities, isolating the complexity from the main CSI determination流程 and making it manageable through dedicated model design.
Data Source
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AI summary
Example embodiments provide signaling and configuration for model-based channel state information reporting by an apparatus. Instead of reporting a table of indices or quantized values in case of a several hypotheses, coefficients of a model for requested channel state information may be reported. Apparatuses, methods, and computer programs are disclosed.