US 12,256,263 B2
Machine learning based adaptation of QoE control policy
Miguel Angel Puente Pestaña, Madrid (ES); and Miguel Angel Muñoz De La Torre Alonso, Madrid (ES)
Assigned to Telefonaktiebolaget LM Ericsson (Publ), Stockholm (SE)
Appl. No. 17/628,616
Filed by Telefonaktiebolaget LM Ericsson (publ), Stockholm (SE)
PCT Filed Sep. 10, 2019, PCT No. PCT/EP2019/074093
§ 371(c)(1), (2) Date Jan. 20, 2022,
PCT Pub. No. WO2021/013368, PCT Pub. Date Jan. 28, 2021.
Claims priority of application No. 19382637 (EP), filed on Jul. 25, 2019.
Prior Publication US 2023/0232272 A1, Jul. 20, 2023
Int. Cl. H04W 28/02 (2009.01); H04W 24/08 (2009.01)
CPC H04W 28/0268 (2013.01) [H04W 24/08 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method in a first node of controlling user data traffic in a wireless communication network, the method comprising:
receiving from a session management function (SMF) a session modification (SM) request and a desired quality of experience (QoE) level for user data traffic of a user of the wireless communication network, the SM request including a quality enforcement rule (QER);
determining an estimated QoE level for the user data traffic subject to the QER;
adapting the QER based at least in part on a reward obtained from a reinforced learning process applied to the desired QoE level and the estimated QoE level; and
applying the adapted QER to the user data traffic.