Telefonaktiebolaget Lm Ericsson (publ)

Tor Kvernvik

Tony Larsson

Mona Matti

Niklas Björk

Mattias Lidström

Simon Moritz

Nicolas Seyvet

Hjalmar Olsson

Jaeseong Jeong

Yu Wang

Christian Skärby

Henrik Nyberg

Martin Isaksson

Raimundas Gaigalas

Johan Haraldson

Walter Müller

Tommy Arngren

Jing Fu

Justus Petersson

Göran Eriksson

Rafia Inam

Henrik Basilier

Yi Cheng

Rickard Cöster

Victor Manuel Avila Gonzalez

Martin Svensson

Selim Ickin

Azadeh Bararsani

Yang Zuo

Belen Pancorbo Marcos

Jonas Björk

Jinghong Yang

Fredrik Hildorsson

Mattias Linstrom

Jonathan Magnusson

Raul Benito Garcia

Burak Demirel

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

Methods and systems for dynamically selecting a link adaptation policy, LAP. In some embodiments, the method includes generating a machine learning, ML, model, wherein generating the ML model comprises providing training data to an ML algorithm. The method further includes using channel quality information, additional information, and the ML model to select a LAP from a set of predefined LAPs. In some embodiments, the additional information comprises: neighbor cell information about a second cell served by a second TRP, distance information indicating a distance between a UE and a first TRP, and/or gain information indicating radio propagation gain between the UE and the serving node. The method further includes the first TRP transmitting second data to the UE using the selected LAP.

Link adaptation optimized with machine learning

A method performed by a first communication device operating in a wireless communications network. The first communication device obtains a first set of one or more values indicating an observed speed of a boat relative to a power of an engine of the boat. The speed and the first indication are obtained by from sensors in the boat. The first communication device then obtains a second indication of an external factor on the hull of the boat causing friction against water. The obtaining of the second indication is based at least on: the obtained first set, and a reference. The reference is based on one of: a) a threshold, and b) a mathematical model. The first communication device also initiates providing a third indication of the external factor to a device, based on the obtained second indication.

Devices and methods for indicating an external factor on the hull of a boat

Methods and systems for dynamically selecting a link adaptation policy, LAP. In some embodiments, the method includes using channel quality information, additional information, and a machine learning, ML, model to select a LAP from a set of predefined LAPs, the set of predefined LAPs comprising a first LAP and a second LAP. In some embodiments, the additional information comprises: neighbor cell information about a second cell served by a second TRP, distance information indicating a distance between a UE and a first TRP, and/or gain information indicating a radio propagation gain between the UE and the serving node. The method further includes the first TRP transmitting data to the UE using the selected LAP.

Link adaptation optimization with contextual bandits

Method performed by a first communication device () operating in a wireless communications network (). The first communication device () obtains () a first set of one or more values indicating an observed speed of a boat () relative to a power of an engine () of the boat (). The speed and the first indication are obtained by from sensors () in the boat (). The first communication device () then obtains () a second indication of an external factor on the hull of the boat () causing friction against water. The obtaining () of the second indication is based at least on: the obtained first set, and a reference. The reference is based on one of: a) a threshold, and b) a mathematical model. The first communication device () also initiates providing () a third indication of the external factor to a device (), based on the obtained second indication.

A method performed by a first wireless device served by a first wireless access point in a first wireless communications network, the first wireless communications network being operated by a first network operator, comprises acquiring () a determination from a first reinforcement learning agent of whether to roam from the first wireless access point to a second wireless access point in a second wireless communications network, the second wireless communications network being operated by a second network operator. The method further includes roaming () from the first wireless access point to the second wireless access point, based on the determination.

Methods and apparatus for roaming between wireless communications networks

Some embodiments herein relate to a method performed by a wireless communication device for managing communication in a wireless communications network. The wireless communication device obtains an indicator indicating a model and one or more trained model parameters for the model, wherein the model is related to an event being one of the following events: a handover procedure, a cell reselection procedure, and a beam reselection procedure. The wireless communication device further selects the model based on the obtained indicator. The wireless communication device executes the selected model using the obtained one or more trained model parameters; and triggers a process, being associated with the event, based on an output of the executed model.

Managing communication in a wireless communications network

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

It is presented a method for managing communication data. The method is performed by a connection manager server () and comprising the step of: causing store () of historical communication data, for a communication session between a user and one or more contacts, into a database (), wherein the historical communication data comprises communication data, a session identifier, a communication type, a communication context, and a user identifier for each contact having participated in the communication session. Methods for indexing communication data, and for recommending a communication type, are also presented. Corresponding connection manager servers (), computer programs () and a computer program product () are also presented.

Managing and indexing communication data, and recommending communication type

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.

Methods and apparatus for analysing performance of a telecommunications network

A method and apparatus for analytics-driven wireless device session context handover in an operator cloud are provided. According to one aspect, some embodiments include a method for a network node to determine whether to perform a wireless device session context handover. The method includes determining, at the network node, a first cost of not handing over a wireless device session context based at least in part on a calculated estimate of time the wireless device will stay associated with a current base station and a calculated estimate of a cost per unit time of not performing the handover. The method also includes determining, at the network node, a second cost of handing over the wireless device session context based at least in part on at least one of context-related data, base station load, backhaul load and backhaul delay.

Analytics driven wireless device session context handover in operator cloud

A method is provided for analyzing data storage applied on at least one data storage type. The data to be stored is transmitted in at least one data stream between at least one application instance and the at least one data storage type. The method includes accessing, from the at least one data stream, data selected according to pre-defined rules. Data access patterns are aggregated on the basis of the selected data, where the data access patterns are indicative of the at least one storage type applied. Classifiers are obtained by applying trained classifiers to the aggregated data access patterns. Differences between the obtained classifiers and the trained classifiers are analyzed to determine, that the obtained classifiers are indicative of at least one data storage type other than a presently used data storage type, in case at least one predefined threshold value is exceeded when analyzing the differences.

Method and system for analyzing accesses to a data storage type and recommending a change of storage type

A network anomaly detector detects a network anomaly by monitoring a communication network and provides an indication of the detected network anomaly to a network anomaly analyzer. The network anomaly analyzer receives the indication of the detected network anomaly and, on the basis of data representing the detected network anomaly and additional data, e.g., from outside the communication network, performs classification of the detected network anomaly. Depending on the classification of the detected network anomaly, the network anomaly analyzer provides a report of the detected network anomaly to another node. If for example the detected network anomaly is classified as expected behavior, reporting of the detected network anomaly may be suppressed.

Classification of detected network anomalies using additional data

A method and apparatus for providing labelling information to a third party regarding terminal users in a communication network. A labelling unit receives communication related data generated from executed communications of the terminal users, and fetches stored labelling rules which have been configured specifically for the third party. The labelling unit then converts the communication related data into labelling information, where a communication habits vector is determined by applying the fetched labelling rules on the received communication related data, and the labelling information is determined for the terminal user(s) based on the resulting communication habits vector. The determined labelling information is finally delivered to the third party.

Method and arrangement in a communication network

The invention provides an Access Selection Server (ASS) and a method for the same. The invention also provides a User Equipment (UE). The Access Selection Server (ASS) comprises storing means and software and is adapted for location in a communication network comprising at least one User Equipment (UE) and communication nodes. The ASS is also arranged to store information of all accesses for each UE in the communication network and information of overall load status of the communication network in the storing means by means of collecting information through interactions with the communication nodes, the UE and an Access Selection Server subscriber Data Base (ASS DB). The ASS is further arranged to communicate control messages, based on the stored information, from the ASS to the UE, thereby enabling control of multiple UE-accesses from the ASS.

Growing community of inventors

Täby, Sweden

Tor Kvernvik