18540239. DEVICE AND METHOD FOR MACHINE LEARNING IN A TELECOMMUNICATIONS NETWORK BASED ON RADIO CELLS simplified abstract (Robert Bosch GmbH)
Contents
- 1 DEVICE AND METHOD FOR MACHINE LEARNING IN A TELECOMMUNICATIONS NETWORK BASED ON RADIO CELLS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DEVICE AND METHOD FOR MACHINE LEARNING IN A TELECOMMUNICATIONS NETWORK BASED ON RADIO CELLS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Machine Learning in Telecommunications Networks
- 1.11 Original Abstract Submitted
DEVICE AND METHOD FOR MACHINE LEARNING IN A TELECOMMUNICATIONS NETWORK BASED ON RADIO CELLS
Organization Name
Inventor(s)
Hugues Narcisse Tchouankem of Hemmingen (DE)
Maximilian Stark of Hamburg (DE)
DEVICE AND METHOD FOR MACHINE LEARNING IN A TELECOMMUNICATIONS NETWORK BASED ON RADIO CELLS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18540239 titled 'DEVICE AND METHOD FOR MACHINE LEARNING IN A TELECOMMUNICATIONS NETWORK BASED ON RADIO CELLS
Simplified Explanation
This patent application describes a method and device for machine learning in a telecommunications network using radio cells. It focuses on seamless connection handover for mobile terminals within the network.
- Observations of signal properties received by the mobile terminal are recorded.
- Observations of signals transmitted by network devices for connection handover are recorded.
- A model is created to estimate a parameter based on these observations.
- The estimated parameter value is determined using the model.
Key Features and Innovation
- Machine learning in a telecommunications network based on radio cells.
- Seamless connection handover for mobile terminals during calls or data connections.
- Recording and analysis of signal properties for efficient handover.
- Creation of a model to estimate parameters for smooth transitions.
- Real-time adaptation of handover processes based on observed signals.
Potential Applications
This technology can be applied in various telecommunications networks to enhance the efficiency of connection handovers. It can improve the overall user experience by ensuring seamless transitions between radio cells.
Problems Solved
- Interruptions during connection handovers in telecommunications networks.
- Inefficient switching processes for mobile terminals.
- Lack of real-time adaptation for optimal handover performance.
Benefits
- Improved call and data connection quality.
- Enhanced user experience with uninterrupted transitions.
- Efficient utilization of network resources.
- Real-time adaptation for dynamic network conditions.
Commercial Applications
Telecommunications Industry Applications
This technology can be utilized by telecommunications companies to optimize their network performance and provide a seamless experience for their customers. It can lead to increased customer satisfaction and retention.
Questions about Machine Learning in Telecommunications Networks
How does machine learning improve connection handover in telecommunications networks?
Machine learning enables the network to adapt in real-time based on observed signal properties, leading to more efficient and seamless connection handovers for mobile terminals.
What are the key benefits of using machine learning for connection handover in telecommunications networks?
The key benefits include improved connection quality, enhanced user experience, efficient resource utilization, and real-time adaptation to network conditions.
Original Abstract Submitted
A device and method for machine learning in a telecommunications network based on radio cells. A connection handover in the telecommunications network, in which a mobile terminal switches from one radio cell of the telecommunications network to another radio cell of the telecommunications network during a call connection or a data connection without interrupting this connection, is carried out as a function of a parameter. A series of observations of a property of a signal received by the mobile terminal in the telecommunications network is recorded. A series of observations of a signal, transmitted by a network device in the telecommunications network, for connection handover is recorded. A model for determining an estimated value for the parameter is determined as a function of the series of observations, and the estimated value is determined with the model.
