View source for 17766317. EFFICIENT 3D MOBILITY SUPPORT USING REINFORCEMENT LEARNING simplified abstract (Telefonaktiebolaget LM Ericsson (publ))

=EFFICIENT 3D MOBILITY SUPPORT USING REINFORCEMENT LEARNING=


==Organization Name==

[[:Category:Telefonaktiebolaget LM Ericsson  (publ)|Telefonaktiebolaget LM Ericsson  (publ)]]


[[Category:Telefonaktiebolaget LM Ericsson  (publ)]]

==Inventor(s)==

[[:Category:Xingqin Lin of SAN JOSE CA (US)|Xingqin Lin of SAN JOSE CA (US)]][[Category:Xingqin Lin of SAN JOSE CA (US)]]

[[:Category:Yun Chen of Austin TX (US)|Yun Chen of Austin TX (US)]][[Category:Yun Chen of Austin TX (US)]]

[[:Category:Mohammad Mozaffari of Fremont CA (US)|Mohammad Mozaffari of Fremont CA (US)]][[Category:Mohammad Mozaffari of Fremont CA (US)]]

[[:Category:Talha Khan of SANTA CLARA CA (US)|Talha Khan of SANTA CLARA CA (US)]][[Category:Talha Khan of SANTA CLARA CA (US)]]

==EFFICIENT 3D MOBILITY SUPPORT USING REINFORCEMENT LEARNING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17766317 titled 'EFFICIENT 3D MOBILITY SUPPORT USING REINFORCEMENT LEARNING

==Simplified Explanation==

**Explanation:**
The patent application describes a method for mobility management in a wireless network using machine learning models to optimize handover operations for wireless devices as they move between cells.

**Bullet Points:**
* Obtain data samples to model the wireless network environment with multiple cells.
* Build a machine learning model of the wireless network using the data samples.
* Train the machine learning model to determine a sequence of handovers for a wireless device moving between cells.
* Receive mobility information for a wireless device.
* Determine handover operations based on the mobility information.
* Transmit the handover operations to the wireless device.

**Potential Applications:**
* Telecommunications industry for optimizing handover operations in wireless networks.
* Internet of Things (IoT) devices that require seamless connectivity while moving between different network cells.

**Problems Solved:**
* Efficient management of handovers in a wireless network.
* Seamless connectivity for mobile devices moving between different network cells.

**Benefits:**
* Improved network performance and reliability.
* Enhanced user experience with seamless connectivity during device mobility.


==Original Abstract Submitted==

According to some embodiments, a method performed by a network node for mobility management comprises obtaining data samples for modeling a wireless network environment that comprises a plurality of cells and building a machine learning model of the wireless network using the obtained data samples. The machine learning model is trained to determine a sequence of handovers for a wireless device among the plurality of cells for the wireless device to traverse from a source cell to a destination cell. The method further comprises receiving mobility information for a wireless device, determining one or more handover operations for the wireless device based on the mobility information, and transmitting the one or more handover operations to the wireless device.



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