CSI Feedback Scheme Selection for Spectral Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a need for improved channel state information (CSI) feedback schemes in 5G New Radio (NR) systems to enhance spectral efficiency, which can also be applicable to other multi-access technologies and telecommunication standards.
Innovation Solution
A method and apparatus for selecting between machine-learning (ML) CSI feedback schemes and non-ML CSI feedback schemes, allowing UEs to report CSI feedback based on instructions from network nodes, and for network nodes to select and transmit instructions for these schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ML-based CSI feedback schemes are used, then spectral efficiency can be improved through intelligent optimization, but computational complexity and processing requirements increase
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that processes channel state information and generates feedback recommendations. This ML model acts as a mediator between the complex wireless channel environment and the feedback mechanism, enabling intelligent optimization of spectral efficiency while managing computational complexity through learned patterns rather than exhaustive processing
Solution Approach 2:
The system dynamically changes operational parameters by selecting between different feedback schemes (ML-based versus traditional) based on channel conditions, traffic patterns, and performance requirements. This parameter adaptation allows the system to optimize spectral efficiency when ML processing is beneficial while falling back to simpler schemes when computational complexity becomes excessive
2Measurement precision
If ML-based CSI feedback schemes are used, then feedback accuracy can be enhanced through learning patterns, but processing time and latency increase
Solution Approach 1:
The machine learning model is pre-trained offline on extensive channel data to learn patterns and relationships in advance. This preliminary action enables the model to make accurate predictions during runtime with minimal processing time, as the heavy computational work of pattern recognition has already been performed during the training phase
Solution Approach 2:
The system dynamically adapts by selecting between ML-based feedback and traditional feedback schemes based on real-time conditions. When channel conditions are stable and patterns are predictable, the ML scheme provides high accuracy. When latency requirements are stringent or conditions are highly dynamic, the system switches to faster traditional schemes, thus balancing accuracy and processing time
3Adaptability or versatility
If selection between ML and non-ML schemes is implemented, then system adaptability is improved, but control and management complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the system monitors performance metrics (spectral efficiency, latency, channel conditions) and uses this feedback to automatically adjust the selection between ML-based and non-ML feedback schemes. This closed-loop control enables adaptability without requiring complex manual management, as the system self-adjusts based on observed performance
Data Source
AI summary
An apparatus may be configured to receive an instruction indicating one of a machine-learning (ML) channel state information (CSI) feedback scheme associated with a first spectral efficiency or a non-ML CSI feedback scheme associated with a second spectral efficiency; and report the CSI feedback to a network node using the indicated one of the ML CSI feedback scheme or the non-ML CSI feedback scheme. Another apparatus may be configured to select one an ML CSI feedback scheme associated with a first spectral efficiency or a non-ML CSI feedback scheme associated with a second spectral efficiency; transmit, to the UE, an instruction indicating the selected one of the ML CSI feedback scheme or the non-ML CSI feedback scheme; and receive, from the UE, the CSI feedback that is associated with the selected one of the ML CSI feedback scheme or the non-ML CSI feedback scheme.