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

EFFICIENT 3D MOBILITY SUPPORT USING REINFORCEMENT LEARNING

Organization Name

Telefonaktiebolaget LM Ericsson (publ)

Inventor(s)

Xingqin Lin of SAN JOSE CA (US)

Yun Chen of Austin TX (US)

Mohammad Mozaffari of Fremont CA (US)

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.