Nokia Technologies Oy

Alcatel Lucent

Ilaria Malanchini

Qi Liao

Suresh Kalyanasundaram

Afef Feki

Veronique Capdevielle

Osman Aydin

Jens Gebert

Fahad Syed Muhammad

Mathieu Boussard

Stefan Valentin

Alessandro Lieto

Systems, methods, apparatuses, and computer program products for dependency-aware quality of service (QoS) profile provisioning. The method may include obtaining information on at least one application data flow, and dependency information between one application and at least one other application. The method may also include receiving, from a network, information on an updated quality of service profile for the at least one application, and information on another updated quality of service profile for the at least one other application. The updated quality of service profile and the another updated quality of service profile may be selected based on the dependency graph.

Dependency-aware quality of service profile provisioning

A deep transfer reinforcement learning (DTRL) method based on transfer learning within a deep reinforcement learning (DRL) framework is provided to accelerate the GoB optimization decisions when experiencing environment changes in the same source radio network agent or when being applied from a source radio network agent to a target radio network agent. The transferability of the knowledge embedded in a pre-trained neural network model as a Q-approximator is exploited, and a mechanism to transfer parameters from a source agent to a target agent is provided, where the transferability criterion is based on the similarity measure between the source and target domain.

Apparatus, method and computer program for accelerating grid-of-beams optimization with transfer learning

An apparatus for use by a communication network control element or function configured to control a setting of parameters of a self-organizing communication network, the apparatus comprising at least one processing circuitry, and at least one memory for storing instructions to be executed by the processing circuitry, wherein the at least one memory and the instructions are configured to, with the at least one processing circuitry, cause the apparatus at least: to obtain a pre-trained network optimization model indicating a mapping between a communication network environment forming an input of the model, optimization actions or decisions forming an output of the model, and system performance indicators forming a reward, to cause sending, to at least one communication network element or function forming a part of the self-organizing communication network, a request for providing similarity data for a similarity analysis allowing to determine a similarity between a part of the self-organizing communication network for which the pre-trained network optimization model is derived and a part of the self-organizing communication network of the at least one communication network element or function to which the request is sent, to perform the similarity analysis for determining a similarity between the part of the self-organizing communication network for which the pre-trained network optimization model is derived and each part of the self-organizing communication network for which similarity data for the similarity analysis are received, to determine, on the basis of the similarity analysis, at least a part of the pre-trained network optimization model to be provided to the at least one communication network element or function forming a part of the self-organizing communication network from which the similarity data are received, and to cause sending of the determined part of the pre-trained network optimization model to the at least one communication network element or function forming a part of the self-organizing communication network from which the similarity data are received.

Procedure for optimization of self-organizing network

A method is provided of replacing a first drone base station with a second drone base station, the first drone base station, the method comprising: sending by the first drone base station first pilot signals indicating a cell identifier; receiving by the first drone base station information that the second drone base station is in the vicinity of the first drone base station; sending by the second drone base station second pilot signals which indicate the same cell identifier as the first drone base station; receiving by the first drone base station from the second drone base station an indication to cease to send first pilot signals; and dependent upon receiving by the first drone base station from the second drone base station the indication to cease to send first pilot signals, ceasing by the first drone base station the sending of first pilot signals.

Seamless replacement of a first drone base station with a second drone base station

Growing community of inventors

Stuttgart, Germany

Ilaria Malanchini