Near-Real-Time Baseband Policy Adaptation for RedCap Devices
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Solution Overview
Problem
Existing network systems struggle to efficiently manage and optimize resources for reduced capability devices (redcap devices) due to the lack of real-time identification and adaptation of their data requirements and capabilities.
Innovation Solution
Implementing a near-real-time RAN intelligent controller to dynamically adjust network policies based on the identification of redcap devices and their specific data requirements, using insights derived from near-real-time RAN intelligence sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network systems use existing static policies to manage reduced capability devices, then device compatibility is maintained, but network resource allocation efficiency deteriorates
Solution Approach 1:
The patent implements dynamic network policies that automatically adapt to the capabilities and requirements of reduced capability devices. The system continuously monitors device status and adjusts resource allocation, QoS parameters, and network configuration in real-time, transforming static policies into dynamic responses that optimize network performance for diverse device types.
Solution Approach 2:
The system changes multiple network parameters including bandwidth allocation, priority levels, QoS settings, and resource assignment based on device capability assessment. By dynamically adjusting these parameters according to device type and requirements, the network optimizes resource utilization while maintaining compatibility across different device capabilities.
2Productivity
If network systems implement real-time identification and adaptation of redcap devices, then network optimization improves, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the network continuously monitors device performance, capability status, and traffic patterns. This feedback information is used to automatically adjust policies and resource allocation, enabling real-time optimization without requiring complex manual configuration. The feedback loop simplifies operation while improving efficiency.
Solution Approach 2:
The system performs self-optimization by automatically detecting device capabilities, assessing requirements, and adjusting network parameters without external intervention. The network intelligence engine autonomously manages policy generation and resource allocation, reducing operational complexity while maintaining high optimization levels.
3Measurement precision
If network systems use near-real-time RAN intelligence sources to determine device requirements, then policy accuracy improves, but information processing time increases
Solution Approach 1:
The system performs preliminary assessment of device capabilities and pre-configures policy templates before actual data processing is needed. By preparing capability profiles, resource allocation plans, and QoS parameters in advance based on device type recognition, the network reduces real-time processing time while maintaining high accuracy in requirement identification.
Solution Approach 2:
The system processes only the essential information needed for policy determination rather than all available data, achieving sufficient accuracy without excessive processing time. The intelligence engine selectively extracts and analyzes critical parameters from RAN intelligence sources, balancing measurement precision with processing efficiency by focusing on high-impact data elements.
Data Source
AI summary
Systems, methods, and computer-readable media herein dynamically adjust the policies used within a core network. These policies are determine based on the identification of a user device being a reduced capability device and the data requirements for that device. A correlation between the type of reduced capability device and the data requirements is used to derive data-drive insights using a near-real time RAN intelligence controller. The data used to determine these insights and policies are based near-real time sources.