US 12,461,994 B2
User plane selection using reinforcement learning
Dinand Roeland, Sollentuna (SE); Andreas Yokobori Sävö, Täby (SE); and Jaeseong Jeong, Solna (SE)
Assigned to TELEFONAKTIEBOLAGET LM ERICSSON (PUBL), Stockholm (SE)
Appl. No. 17/636,990
Filed by Telefonaktiebolaget LM Ericsson (publ), Stockholm (SE)
PCT Filed Aug. 30, 2019, PCT No. PCT/SE2019/050813
§ 371(c)(1), (2) Date Feb. 21, 2022,
PCT Pub. No. WO2021/040592, PCT Pub. Date Mar. 4, 2021.
Prior Publication US 2022/0358335 A1, Nov. 10, 2022
Int. Cl. G06F 18/21 (2023.01); G06F 17/16 (2006.01)
CPC G06F 18/217 (2023.01) [G06F 17/16 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method of reinforcement learning for placement of a plurality of service functions at nodes of a telecommunications network, the method comprising:
defining a state of a system by means of an allocation matrix, wherein the state of the system includes an indication of service functions supported in the system, wherein:
each first vector of the allocation matrix corresponds to a respective one of the nodes of the telecommunications network,
each second vector of the allocation matrix corresponds to a respective one of the plurality of service functions, and
each cell of the allocation matrix contains a value 1 if the one of the plurality of service functions corresponding to the respective second vector is placed on the one of the nodes of the telecommunications network corresponding to the respective first vector, and otherwise contains a value 0; and
utilizing the allocation matrix as input to train a reinforcement learning (RL) agent to function as a placement algorithm for placing the plurality of service functions among the nodes of the telecommunication network.