AI-Assisted Wireless Configuration for Beam and CSI Optimization
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Solution Overview
Problem
Determination of adaptive network configuration in wireless communication systems, such as beam management, channel state information feedback, and wireless terminal positioning, requires lengthy measurement processes and significant computation power, which can be inefficient and resource-intensive.
Innovation Solution
Implementing an artificial intelligence framework for AI-assisted network configuration (AICS) that provides lifecycle management of AI models, including training, delivery, activation, inference, and deactivation, via control and data messages between network elements to facilitate agile and efficient network configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional measurement and computation methods are used for adaptive network configuration, then measurement precision and reliability are improved, but loss of time and use of energy worsen due to lengthy measurement processes and significant computation power requirements
Solution Approach 1:
The system performs preliminary actions by pre-training AI models offline with extensive measurement data and computation, then deploying these trained models for rapid real-time inference. This transfers the computationally intensive work to a preliminary phase, allowing fast configuration decisions during actual operation without sacrificing accuracy
Solution Approach 2:
An AI model serves as an intermediary between raw measurement data and network configuration decisions. The model processes input features and generates configuration recommendations, replacing direct traditional measurement-computation pipelines with an intelligent intermediary that accelerates the decision-making process while maintaining precision
2Measurement precision
If traditional measurement and computation methods are used for adaptive network configuration, then measurement precision is improved, but use of energy worsens due to significant computation power requirements
Solution Approach 1:
The system performs preliminary actions by pre-training AI models offline with extensive measurement data and computation, then deploying these trained models for rapid real-time inference. This transfers the computationally intensive work to a preliminary phase, allowing fast configuration decisions during actual operation without sacrificing accuracy
Solution Approach 2:
The patent replaces traditional mechanical/computational systems with an AI-based system. Instead of using conventional computation methods that require significant processing power, the system uses trained neural networks that can make configuration decisions with minimal real-time computation, substantially reducing energy consumption during operation
3Productivity
If AI models are deployed for network configuration, then productivity is improved by reducing measurement and computation requirements, but device complexity worsens due to AI model provisioning and management overhead
Solution Approach 1:
The patent extracts the complex AI model management functions from the network configuration system and places them in a dedicated AI model repository and management platform. This separation allows the core configuration system to benefit from AI acceleration while the complexity of model provisioning, versioning, and updates is handled by a specialized external system
Solution Approach 2:
The AI model repository and management system serves multiple functions: storing trained models, provisioning models to appropriate network elements, managing model versions, monitoring model performance, and updating models. This multi-functional platform handles diverse AI management tasks through a unified system, reducing overall complexity
Data Source
AI summary
This disclosure is generally directed to wireless communication systems and methods and relates particularly to a mechanism for implementing an artificial intelligence framework for adaptively configure the over-the-air communication interfaces of the wireless communication systems. AI network configuration functions may be provided as services for AI-assisted network configuration (AI configuration services, or AICS). Such AICS may be requested and configured via various messaging and signaling mechanisms. The AI model life cycle management including training, delivery, activation, inference, deactivation, switching, performance evaluation, and the like may be configured, triggered, and otherwise provisioned via control and data messages and signaling communicated between the various network elements in the wireless communication system.