View source for Robert bosch gmbh (20240214885). DEVICE AND METHOD FOR MACHINE LEARNING IN A TELECOMMUNICATIONS NETWORK BASED ON RADIO CELLS simplified abstract

=DEVICE AND METHOD FOR MACHINE LEARNING IN A TELECOMMUNICATIONS NETWORK BASED ON  RADIO CELLS=


==Organization Name==

[[:Category:robert bosch gmbh|robert bosch gmbh]]


[[Category:robert bosch gmbh]]

==Inventor(s)==

[[:Category:Hugues Narcisse Tchouankem of Hemmingen (DE)|Hugues Narcisse Tchouankem of Hemmingen (DE)]][[Category:Hugues Narcisse Tchouankem of Hemmingen (DE)]]

[[:Category:Maximilian Stark of Hamburg (DE)|Maximilian Stark of Hamburg (DE)]][[Category: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 20240214885 titled 'DEVICE AND METHOD FOR MACHINE LEARNING IN A TELECOMMUNICATIONS NETWORK BASED ON  RADIO CELLS

==Simplified Explanation==
This patent application describes a device and method for machine learning in a telecommunications network using radio cells. It focuses on seamless connection handover for mobile terminals switching between radio cells during calls or data connections without interruption.

* Observations of signal properties received by the mobile terminal and transmitted by network devices are recorded.
* A model is created to estimate a parameter based on these observations, allowing for smooth connection handover.
* 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 adjustment of connections without interruption.

==Potential Applications==
This technology can be applied in:
* Mobile telecommunications networks
* IoT networks
* Industrial automation systems
* Smart city infrastructure
* Emergency response networks

==Problems Solved==
* Seamless handover between radio cells without interruption.
* Efficient use of network resources.
* Improved user experience during calls or data connections.
* Real-time adaptation to changing network conditions.
* Enhanced reliability of connections.

==Benefits==
* Enhanced user experience with uninterrupted connections.
* Optimized network performance and resource utilization.
* Increased reliability and efficiency in telecommunications networks.
* Real-time adaptation to network conditions for seamless transitions.
* Improved overall network quality and service delivery.

==Commercial Applications==
'''Seamless Connection Handover Technology in Telecommunications Networks'''
This technology can revolutionize the telecommunications industry by providing seamless handover between radio cells, ensuring uninterrupted connections for users. It can be integrated into mobile network infrastructure, IoT systems, and various other applications requiring reliable and efficient connectivity.

==Prior Art==
Potential areas to explore for prior art related to this technology include:
* Machine learning in telecommunications networks
* Seamless connection handover in mobile networks
* Signal analysis for network optimization

==Frequently Updated Research==
Stay updated on the latest advancements in machine learning for telecommunications networks, signal processing for connection handover, and real-time network optimization techniques to enhance the efficiency and reliability of telecommunications services.

==Questions about Seamless Connection Handover Technology==
===How does this technology improve user experience in telecommunications networks?===
This technology ensures seamless handover between radio cells, providing uninterrupted connections for users during calls or data sessions, enhancing their overall experience.

===What are the potential applications of this technology beyond telecommunications networks?===
Apart from telecommunications, this technology can be applied in IoT networks, industrial automation, smart city infrastructure, and emergency response systems for efficient and reliable connectivity.


==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.



[[Category:H04W36/00]]
[[Category:G06N20/00]]
[[Category:CPC_H04W36/00833]]