Radio Exposure Function for Dynamic RAN Network Slicing
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Solution Overview
Problem
Next-generation telecommunications networks face challenges in securely exposing services and capabilities while addressing varying communication needs of different types of connected devices, leading to network congestion and increased greenhouse gas emissions.
Innovation Solution
Implementing a radio exposure function as a network abstraction layer that decentralizes RAN functions, automates radio resource management, and uses machine learning to dynamically allocate resources and network slices, enhancing RAN intelligent controllers with beamforming and traffic steering capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If next-generation networks connect more devices with faster speeds and lower latencies, then network capacity and service capabilities are improved, but network congestion increases and performance deteriorates in densely populated areas
Solution Approach 1:
The patent segments the network into multiple network slices, each optimized for specific services or applications. This allows different traffic types to be handled by dedicated logical networks, preventing congestion in one slice from affecting others and maintaining overall network reliability while supporting high capacity
Solution Approach 2:
The patent introduces a virtual dimension through network slicing, creating multiple logical networks over a single physical infrastructure. This dimensional transformation allows simultaneous support for diverse services with different requirements without physical network expansion, resolving the contradiction between capacity and performance
2Productivity
If next-generation networks handle larger volume of data and devices, then service capabilities are enhanced, but greenhouse gas emissions increase
Solution Approach 1:
The patent creates a universal network slicing framework that can accommodate multiple service types and applications within a single physical network infrastructure. This multi-functionality allows the network to handle diverse data volumes efficiently without requiring separate physical networks for each service, thereby reducing overall energy consumption and emissions while maintaining high productivity
Solution Approach 2:
The patent dynamically adjusts network parameters such as resource allocation, bandwidth, and priority levels across different network slices based on real-time demands. This parameter optimization ensures efficient resource utilization, preventing waste and reducing the energy required to handle data traffic, thus lowering greenhouse gas emissions while maintaining high data handling capacity
Data Source
AI summary
A programmable radio exposure function switches between real-time and near-real-time modes of operation for a radio access network (RAN) intelligent controller of a telecommunications system. An application programming interface is exposed by the radio exposure function and performs radio resource management for the telecommunications system. The application programming interface communicates with services and/or applications to control RAN functions, and allocates RAN resources of the telecommunications system to a user equipment for the services and/or applications. A machine learning module is embedded within the radio exposure function and trained to identify network slices of the telecommunications system for the services and/or applications. The machine learning module analyzes data received from the user equipment to detect that the user equipment has violated a security constraint of the telecommunications system.