Telefonaktiebolaget Lm Ericsson (publ)

Jaeseong Jeong

Yu Wang

Tor Kvernvik

Johan Haraldson

Dinand Roeland

Rodrigo Correia

Vladimir Verbulskii

Matthew John Lawrenson

Tony Larsson

Göran Eriksson

Maksym Girnyk

Meral Shirazipour

Mona Matti

Julien Forgeat

Kun Wang

Martin Isaksson

Heunchul Lee

Ursula Challita

Adriano Mendo Mateo

Jose Outes Carnero

Wenfeng Hu

Alexandros Nikou

Pedro Batista

Filippo Vannella

Andreas Yokobori Sävö

Sandra Bäckström

Ezeddin Al Hakim

Mathhew John Lawrenson

A method of reinforcement learning is used for placement of a plurality of service functions at nodes of a telecommunications network. The state of the system is defined by an allocation matrix, 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. Moreover, 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.

User plane selection using reinforcement learning

There is disclosed a method of operating a beam-forming wireless communication system, the system has a plurality of radio nodes, an actor neural network being associated to each radio node, wherein further to each actor neural network, there is associated a critic network. The method includes training each actor neural network, for controlling at least one associated radio node, based on learning feedback provided by its associated critic network, the learning feedback being based on operation information provided be the actor neural network for the critic network. The disclosure also pertains to related devices and methods.

Multi-agent policy machine learning

A computing device for supporting an Augmented-Reality (AR) software application is provided. The computing device is operative to select a physical location for placing a current virtual object, where the current virtual object appears to be placed when overlaid onto a video sequence capturing a physical scene in the surroundings of a user, based on an expected physical location which the user assumes in response to displaying the physical scene and the overlaid current virtual object to the user, and an attribute which is spatially dependent and which has an impact on a user experience of the AR software application. Taking the spatially-dependent attribute at the expected physical location of the user into consideration in selecting physical locations for placing virtual objects is advantageous in that virtual objects may be deployed at physical locations so as to provide an improved, or at least satisfactory, user experience to the users.

Supporting an augmented-reality software application

A method performed by a computer system for a telecommunications network. The computer system can access a network metrics repository to retrieve a baseline dataset collected from a baseline policy deployed in the telecommunications network for controlling a configurable parameter of the telecommunications network. The configurable parameter includes an antenna tilt degree. The baseline dataset includes key performance indicators (KPIs) that include KPIs having a continuous value and a plurality of historical changes made to the configurable parameter. The computer system can train a policy model while offline the telecommunications network using the baseline dataset and inverse propensity scoring on the input KPIs having continuous values to output from the policy model a probability of actions for controlling the configurable parameter. A method performed by network node or network nodes is also provided for using a trained policy model to control the configuration parameter.

Methods for controlling a configuration parameter in a telecommunications network and related apparatus

A method and system of filtering abnormal network parameter values are described. A network node receives first values of first network parameters that are not trusted by the network node. The network node determines, based on an expected correlation between the first values of the first network parameters and second values of second network parameters, whether the first values of the first network parameters are anomalies. Responsive to determining that the first values of the first network parameters are not anomalies, the network node causes the first values to be used as input to train a network prediction model that is used for optimization of traffic in the network, and responsive to determining that the first values of the first network parameters are anomalies, the network node causes the first values to not be used to train the network prediction model.

Method and system for filtering of abnormal network parameter values prior to being used in training of a prediction model in a communication network

A node in a communications network is configured to use a reinforcement learning agent to manage a process associated with the communications network. The node is further configured to adjust a value of an exploration rate associated with training of the reinforcement learning agent, based on a performance indicator associated with the managed process.

Methods and nodes in a communications network

A network metrics repository stores cell performance metrics and rule-based data measured during operation of a communication network. A policy neural network circuit has an input layer having input nodes, a sequence of hidden layers, and at least one output node. A processor trains the policy neural network circuit to approximate a baseline rule-based policy for controlling a tilt angle of a remote electrical tilt (RET) antenna based on the rule-based data. The processor provides a live cell performance metric to input nodes, adapts weights that are used by the input nodes responsive to output of the output node, and controls operation of the tilt angle of the RET antenna based on the output The output node provides the output responsive to processing a stream of cell performance metrics through the input nodes. The processor controls operation of the RET antenna based on the output.

Neural network circuit remote electrical tilt antenna infrastructure management based on probability of actions

A de-jitter function for holding-and-forwarding packets such that the packets are delivered with an agreed fixed latency. The de-jitter function can be placed at the edge of a virtual 5G TSN switch (e.g. the de-jitter function can be deployed as part of a UPF for uplink (UL) packets and/or it can be deployed as part of a user equipment (UE) for downlink (DL) packets). By using the de-jitter function, the TSN can consider the wireless network as having a consistent, deterministic latency with no jitter.

Communication system with de-jitter buffer for reducing jitter

A method performed by a first network node operating in a communications network is disclosed. The method is for handling a performance of a radio access network (RAN) including one or more radio network nodes. The first network node determines a configuration of one or more parameters in the RAN based on one or more machine-implemented reinforcement learning (RL) procedures to optimize the performance of the RAN based on the one or more parameters. The RL procedures are further based on at least one of: i) one or more physical characteristics of a deployment of the RAN, ii) one or more radio characteristics of the RAN, and iii) a location of users or traffic load in the RAN. The first network node then initiates providing one or more indicators of the determined configuration to a second network node.

First network node, third network node, and methods performed thereby, for handling a performance of a radio access network

Embodiments of the disclosure provide methods, apparatus and computer programs for generating a radio coverage map. A method comprises: obtaining image data of a geographical area, the image data comprising: a representation of the environment in the geographical area; and an indication of one or more transmission point locations corresponding to the locations of one or more transmission points in a wireless communications network; and applying a generative model to the image data, to generate a radio coverage map of the geographical area.

Radio coverage map generation

A method for enabling forecasting of key performance indicators, KPIs, in a communication network is presented. The method is performed by a network node and comprises collecting (S) time series data of KPIs in the communication network, extracting (S) k-principal components of the collected time series data, determining (S) a principal component score, PCS, from the collected time series data and the extracted k-principal components, and building (S) a PCS forecast model from the determined PCS and period of time-attributes. Network nodes and computer program products thereof are also presented.

Forcasting time series data

A method for analysing performance of a telecommunications network is disclosed. The method comprises assembling values observed for a network performance parameter over a time window and constructing a time series by associating each performance parameter value with a representation of a time at which the value was observed. The method further comprises encoding the time series to generate a compressed representation of the time series, decoding the compressed representation of the time series to reconstruct the time series, calculating a reconstruction error between the time series and the reconstructed time series, and analysing the reconstruction error to determine if the assembled performance parameter values indicate an anomalous behaviour during the time window. Also disclosed are an apparatus and a computer program product configured to conduct methods for analysing performance of a telecommunications network.

Growing community of inventors

Solna, Sweden

Jaeseong Jeong