AI Framework for Wireless CSI Reporting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless communication networks face challenges in configuring artificial intelligence (AI) frameworks for efficient channel state information (CSI) measurement and reporting.
Innovation Solution
The method involves receiving an indication of an AI-based framework at a user equipment, receiving configuration information with at least one parameter of the framework, and communicating an AI report based on this information, which includes a set of values, an indication of a subset of channel resources, or a combination thereof.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional CSI measurement and reporting methods are used, then the system is simple to implement, but throughput and reliability are limited
Solution Approach 1:
The patent introduces an artificial intelligence framework as an intermediary layer between the wireless communication system and traditional CSI measurement methods. This AI framework processes channel state information and generates optimized reporting strategies, enabling improved throughput and reliability while managing system complexity through automated decision-making algorithms.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting AI framework parameters such as neural network architecture, training data selection, and reporting thresholds based on channel conditions and network requirements. This allows the system to optimize performance metrics like throughput and reliability while adapting to varying operational scenarios.
2Measurement precision
If detailed CSI reporting is implemented, then measurement precision improves, but signaling overhead increases
Solution Approach 1:
The patent extracts only the most critical and relevant CSI parameters for reporting by using the AI framework to identify and select key channel state information elements. This extraction approach maintains measurement precision for essential parameters while eliminating redundant information, thereby reducing signaling overhead and optimizing the balance between precision and information efficiency.
3Measurement precision
If AI framework is configured with multiple parameters, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-configuring and pre-training the AI framework with optimized parameter sets before deployment. The framework includes pre-established neural network architectures, pre-selected feature sets, and pre-defined reporting configurations that are tailored to specific wireless scenarios. This preliminary preparation reduces the complexity of real-time configuration while maintaining high measurement precision through proven parameter combinations.
Data Source
AI summary
Apparatuses, methods, and systems are disclosed for configuring an AI based framework. One method includes receiving, an indication indicating an artificial intelligence based framework. The method includes receiving configuration information corresponding to the artificial intelligence based framework. The configuration information comprises at least one parameter of the artificial intelligence based framework. The method includes communicating an artificial intelligence report corresponding to the artificial intelligence based framework based on the configuration information. The artificial intelligence report includes: a set of values corresponding to the configuration information; an indication of a subset of a set of channel resources; or a combination thereof. The artificial intelligence report corresponds to a usage value that describes an artificial intelligence based application.