US 12,335,763 B2
Methods for controlling a configuration parameter in a telecommunications network and related apparatus
Jaeseong Jeong, Solna (SE); Filippo Vannella, Stockholm (SE); Rodrigo Correia, Athlone (IE); and Vladimir Verbulskii, Athlone (IE)
Assigned to TELEFONAKTIEBOLAGET LM ERICSSON (PUBL), Stockholm (SE)
Appl. No. 17/774,124
Filed by Telefonaktiebolaget LM Ericsson (publ), Stockholm (SE)
PCT Filed Nov. 9, 2020, PCT No. PCT/EP2020/081442
§ 371(c)(1), (2) Date May 3, 2022,
PCT Pub. No. WO2021/089863, PCT Pub. Date May 14, 2021.
Claims priority of provisional application 62/932,870, filed on Nov. 8, 2019.
Claims priority of provisional application 62/967,096, filed on Jan. 29, 2020.
Prior Publication US 2022/0394531 A1, Dec. 8, 2022
Int. Cl. H04W 24/10 (2009.01); G06N 3/02 (2006.01); H01Q 1/12 (2006.01); H04L 41/12 (2022.01); H04W 24/02 (2009.01)
CPC H04W 24/10 (2013.01) [G06N 3/02 (2013.01); H01Q 1/125 (2013.01); H04L 41/12 (2013.01); H04W 24/02 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A computer implemented method performed by a computer system for a telecommunications network, the method comprising:
accessing a network metrics repository to retrieve a baseline dataset from a baseline policy deployed in the telecommunications network for controlling a configurable parameter of the telecommunications network, wherein the baseline dataset comprises a plurality of key performance indicators (KPIs) that each have a continuous value, and a plurality of historical changes made to the configurable parameter;
training a policy model while offline the telecommunications network using the baseline dataset and inverse propensity score, pi, on the plurality of KPIs as inputs to output from the policy model a probability of actions for controlling the configurable parameter, wherein training the policy model while offline further comprises splitting the baseline dataset into a training dataset and a testing dataset and validating performance of the probability of actions of the policy model based on comparison with performance of the probability of actions of the testing dataset; and
deploying the trained policy model to a plurality of cells in the telecommunications network via a plurality of network nodes for controlling the configurable parameter of the telecommunications network.