17956975. INTELLIGENT MACHINE LEARNING (ML)-ENABLED END-TO-END (E2E) AUTOMATED ORCHESTRATION FOR COLLABORATIVE NEXT-GENERATION WIRELESS WIRELINE CONVERGENCE (WWC) simplified abstract (AT&T Intellectual Property I, L.P.)
Contents
- 1 INTELLIGENT MACHINE LEARNING (ML)-ENABLED END-TO-END (E2E) AUTOMATED ORCHESTRATION FOR COLLABORATIVE NEXT-GENERATION WIRELESS WIRELINE CONVERGENCE (WWC)
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INTELLIGENT MACHINE LEARNING (ML)-ENABLED END-TO-END (E2E) AUTOMATED ORCHESTRATION FOR COLLABORATIVE NEXT-GENERATION WIRELESS WIRELINE CONVERGENCE (WWC) - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
INTELLIGENT MACHINE LEARNING (ML)-ENABLED END-TO-END (E2E) AUTOMATED ORCHESTRATION FOR COLLABORATIVE NEXT-GENERATION WIRELESS WIRELINE CONVERGENCE (WWC)
Organization Name
AT&T Intellectual Property I, L.P.
Inventor(s)
Eshrat Huda of Hillsborough NJ (US)
David H. Lu of Morganville NJ (US)
Moshiur Rahman of Marlboro NJ (US)
INTELLIGENT MACHINE LEARNING (ML)-ENABLED END-TO-END (E2E) AUTOMATED ORCHESTRATION FOR COLLABORATIVE NEXT-GENERATION WIRELESS WIRELINE CONVERGENCE (WWC) - A simplified explanation of the abstract
This abstract first appeared for US patent application 17956975 titled 'INTELLIGENT MACHINE LEARNING (ML)-ENABLED END-TO-END (E2E) AUTOMATED ORCHESTRATION FOR COLLABORATIVE NEXT-GENERATION WIRELESS WIRELINE CONVERGENCE (WWC)
Simplified Explanation
The abstract describes a system that includes a cross-segment slice controller (CSSC) interfacing with a software-defined network (SDN) controller and a software-defined radio (SDR) controller. The system also includes a machine learning (ML) component and an intelligent end-to-end orchestration platform (IEOP) for dynamic cross-segment network slice management.
- The system includes a cross-segment slice controller (CSSC) that interfaces with an SDN controller and an SDR controller.
- A machine learning (ML) component is used to obtain and analyze data regarding the core network and the radio access network (RAN).
- An intelligent end-to-end orchestration platform (IEOP) coordinates with the SDN controller and the SDR controller via the CSSC to provide dynamic cross-segment network slice management.
Potential Applications
This technology could be applied in telecommunications networks, specifically in managing network slices for improved performance and resource allocation.
Problems Solved
This technology solves the challenge of efficiently managing network slices in a dynamic and intelligent manner to optimize network performance.
Benefits
The benefits of this technology include enhanced network performance, improved resource allocation, and dynamic management of network slices based on real-time data analysis.
Potential Commercial Applications
Potential commercial applications of this technology include telecommunications companies looking to optimize their network performance and resource allocation through intelligent network slice management.
Possible Prior Art
One possible prior art could be existing systems that use machine learning and software-defined networking for network management, but the specific combination of components and functionalities described in this patent application may be novel.
What are the specific functionalities of the machine learning component in this system?
The machine learning component is configured to obtain and analyze data regarding the core network and the radio access network (RAN) to provide insights for dynamic cross-segment network slice management. It may use algorithms to predict network behavior, optimize resource allocation, and improve overall network performance.
How does the intelligent end-to-end orchestration platform (IEOP) interact with the SDN controller and the SDR controller?
The IEOP coordinates with the SDN controller and the SDR controller via the cross-segment slice controller (CSSC) based on outputs of the machine learning component. It uses these outputs to provide dynamic cross-segment network slice management, ensuring efficient communication and resource allocation between the core network and the radio access network.
Original Abstract Submitted
Aspects of the subject disclosure may include, for example, a system including a cross-segment slice controller (CSSC) configured to interface with a software-defined network (SDN) controller and a software-defined radio (SDR) controller. The SDN controller may be associated with a core network and the SDR controller may be associated with a radio access network (RAN). The system further includes a machine learning (ML) component configured to obtain and analyze data regarding the core network and the RAN, and an intelligent end-to-end (E2E) orchestration platform (IEOP) configured to coordinate with the SDN controller and the SDR controller via the CSSC based on outputs of the ML component to provide dynamic cross-segment network slice management. Other embodiments are disclosed.
