Telefonaktiebolaget Lm Ericsson (publ)

Yun Chen

Xingqin Lin

Talha Khan

Mohammad Mozaffari

Mehrnaz Afshang

Tahla Khan

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.

Efficient 3D mobility support using reinforcement learning

A method and apparatus for design and optimization of a network are described. A first deep neural network is used to obtain a function that represents a relationship between design parameters of the network and network performance metrics of the network. A second deep neural network is used to obtain a subset of one or more candidate network deployment configurations that optimize the performance metrics for the network. An optimal candidate network deployment configuration for the network is selected from the subset of candidate network deployment configurations wherein the optimal candidate network deployment configuration maximizes performance of the network as defined based on the performance metrics.

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Austin, TX, United States of America

Yun Chen