AI-Based RRC Idle State Cell Reselection for Lower Interference
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Solution Overview
Problem
Wireless communication networks face interference and congestion issues due to increasing demand for mobile broadband access, which degrades performance and affects both downlink and uplink transmissions.
Innovation Solution
Implementing artificial intelligence (AI) models for enhanced Radio Resource Control (RRC) IDLE and INACTIVE state operations in wireless communication devices, allowing for improved cell selection and reselection based on mobility patterns and other status factors, thereby optimizing network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cell selection and reselection procedures are used in RRC IDLE and INACTIVE states, then devices can maintain basic connectivity, but network interference increases and network congestion worsens due to inefficient resource management
Solution Approach 1:
The AI model performs cell selection and reselection decisions in advance during IDLE and INACTIVE states, predicting optimal cells before actual data transmission occurs. This preliminary action prevents devices from selecting suboptimal cells, thereby reducing network interference and congestion before they can occur.
Solution Approach 2:
The AI model enables devices to autonomously perform intelligent cell selection and reselection without requiring continuous network control signaling. Each device independently makes optimized decisions based on the AI model, reducing overall network signaling overhead and interference while improving connectivity reliability.
2Loss of time
If AI models are implemented for IDLE/INACTIVE state procedures, then latency is reduced and throughput increases, but device complexity and processing requirements increase
Solution Approach 1:
The AI model is configured and prepared in advance during IDLE and INACTIVE states, so that when state transitions or cell reselections are needed, decisions can be made immediately without real-time computation delays. This preliminary preparation reduces latency while the model complexity is managed through offline training and configuration.
Solution Approach 2:
The AI model uses configurable parameters and can be optimized for different device capabilities. By adjusting model complexity parameters and selecting appropriate model sizes, the system achieves low-latency performance while adapting to varying device processing capabilities, thus managing the trade-off between speed and complexity.
3Productivity
If AI-based cell selection optimization is implemented, then network throughput increases, but device power consumption increases due to additional processing
Solution Approach 1:
The AI model performs cell selection and optimization decisions during IDLE and INACTIVE states when the device is already consuming power for basic monitoring. By utilizing this existing power consumption for AI processing rather than activating additional high-power modes, the system achieves throughput improvement with minimal additional energy cost.
Solution Approach 2:
The device uses its own local AI model to make autonomous cell selection decisions without requiring continuous communication with the network for guidance. This self-service approach eliminates the need for frequent signaling exchanges, reducing overall power consumption while maintaining high throughput through optimized cell selection.
Data Source
AI summary
This disclosure provides systems, methods, and devices for wireless communication that support AI model-based enhancements for RRC IDLE and INACTIVE state operations. In a first aspect, a method of wireless communication includes receiving, by a wireless communication device, artificial intelligence (AI) model configuration information for IDLE/INACTIVE state procedures; retrieving, by the wireless communication device, an AI model for IDLE/INACTIVE state procedures based on the AI model configuration information; and performing, by the wireless communication device, one or more IDLE/INACTIVE state procedures based on the AI model. Other aspects and features are also claimed and described.