US 12,245,086 B2
Efficient 3D mobility support using reinforcement learning
Xingqin Lin, San Jose, CA (US); Yun Chen, Austin, TX (US); Mohammad Mozaffari, Fremont, CA (US); and Talha Khan, Santa Clara, CA (US)
Assigned to Telefonaktiebolaget LM Ericsson (publ), Stockholm (SE)
Appl. No. 17/766,317
Filed by Telefonaktiebolaget LM Ericsson (PUBL), Stockholm (SE)
PCT Filed Oct. 5, 2020, PCT No. PCT/IB2020/059333
§ 371(c)(1), (2) Date Apr. 4, 2022,
PCT Pub. No. WO2021/064713, PCT Pub. Date Apr. 8, 2021.
Claims priority of provisional application 62/911,047, filed on Oct. 4, 2019.
Prior Publication US 2024/0056933 A1, Feb. 15, 2024
Int. Cl. H04W 36/00 (2009.01); H04W 36/24 (2009.01); H04W 36/32 (2009.01)
CPC H04W 36/0058 (2018.08) [H04W 36/0083 (2013.01); H04W 36/0055 (2013.01); H04W 36/24 (2013.01); H04W 36/322 (2023.05)] 22 Claims
OG exemplary drawing
 
1. A method performed by a network node for mobility management, the method comprising:
obtaining data samples for modeling a wireless network environment that comprises a plurality of cells;
building a machine learning model of the wireless network using the obtained data samples, wherein 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;
receiving mobility information for a wireless device, wherein the mobility information is used as a state of the machine learning model;
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.