Telefonaktiebolaget Lm Ericsson (publ)

Martin Isaksson

Hjalmar Olsson

Karl Norrman

Tor Kvernvik

Eric Andersson

Henning Wiemann

Walter Müller

Torsten Dudda

Reem Karaki

Ying Sun

Yu Wang

Fredrik Persson

Rickard Cöster

Anders Ohlsson

Jaeseong Jeong

Azadeh Bararsani

Malin Ljungberg

Mats A Jansson

A method performed by a co-ordination network entity in a communications network includes transmitting a request message to a network registration entity in the communications network for identification information for a plurality of candidate network entities in the communications network capable of performing collaborative learning, and receiving identification information for the plurality of candidate network entities from the network registration entity. The method further includes initiating, at one or more network entities of the plurality of candidate network entities, training of a model using a machine-learning algorithm as part of a collaborative learning process.

Methods, apparatus and machine-readable media relating to machine-learning in a communication network

A method performed by a first network entity in a communications network is provided. The method comprises receiving a request from a second network entity, the request comprising one or more selection criteria for selecting network entities to participate in a collaborative learning process to train a model using a machine learning algorithm. The method further comprises transmitting a response message comprising an indication of whether or not the first network entity satisfies the one or more selection criteria.

The present invention relates to a method for predicting a performance indicator for a service in a network. The method is performed by a network node of the network, and the method comprises obtaining measurement data of a metric affecting a service communicating via a radio access network, RAN, node, wherein the metric is independent of the service communicating via the RAN node, inputting the obtained measurement data into a prediction model for performance of the service communicating via the RAN node, wherein the prediction model has been trained with measurement data from the RAN node and measurement data from an end node, and predicting the performance indicator for performance of the service in the network. A network node, a computer program and a computer program product are also presented.

Prediction of a performance indicator

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

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

The present disclosure generally relates to the field of queue management. More specifically, the present disclosure relates to a technique of adjusting adaptive queue management operation in a wireless communication network. A method embodiment comprises determining (S), by an access network node () of the wireless communication network, whether an increase in capacity for data transmission between the access network node () and a wireless communication device () is expected, and adjusting (S), by the access network node (), AQM operation associated with the access network node (), if it is determined that an increase in capacity for data transmission between the access network node () and the wireless communication device () is expected.

Predictive adaptive queue management

For controlling radio transmission in a cellular network, a network node detects data traffic between the cellular network and a user equipment. The detected data traffic is based on a transport protocol which involves transmission of an acknowledgement to acknowledge successful reception of data by the user equipment. The network node estimates a time at which sending of an future acknowledgement by the user equipment is expected. On the basis of the estimated time, the network node allocates UL radio resources to the user equipment. By sending an uplink grant, the network node may indicate the allocated uplink radio resources to the user equipment.

Predictive scheduling for uplink transmission in a cellular network

A modified token bucket algorithm in a rate shaper in a wireless communication network allows for the 'borrowing' of tokens, creating the possibility of a token debt, or a token bucket with a negative Token Bucket Counter (TBC) value. In this modified token bucket algorithm, an incoming packet is passed along so long as the TBC is positive, even if the packet must 'borrow' some tokens, driving the TBC negative. Subsequent incoming packets are stalled until the TBC reaches a positive value. The token bucket refills at a known rate; accordingly, the duration of traffic stalling, when the TBC is negative, is known. During this time, the UE is forced into DRX mode, saving battery power by not monitoring DPCCH for traffic that has been halted. The DRX, or sleep, mode may be invoked in several ways.

Rate shaping triggered discontinuous transmission in wireless communications

A system and method are provided for managing a queue of packets transmitted from a sender to a receiver across a communications network. The sender has a plurality of sender states and a queue manager situated in between the sender and receiver may have a corresponding plurality of queue manager states. The queue manager has one or more queue management parameters which may have distinct predetermined values for each of the queue manager states. When the queue manager detects an event that is; indicative of a change in the sender's state, the queue manager may change its sn tate correspondingly.

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Stockholm, Sweden

Martin Isaksson