Telefonaktiebolaget Lm Ericsson (publ)

Maxim Teslenko

Athanasios Karapantelakis

Konstantinos Vandikas

Aneta Vulgarakis Feljan

Leonid Mokrushin

Volodya Grancharov

Rafia Inam

Sigurdur Sverrisson

Elena Fersman

Vlasios Tsiatsis

Qiang Li

Lackis Eleftheriadis

Ioannis Fikouras

Hongxin Liang

Keven Wang

Erlendur Karlsson

Marin Orlic

Ioanna Pappa

Yifei Jin

Craig Donovan

Ricardo Souza

Sibel Tombaz

Neiva Lindqvist

Daniel Lindström

Gábor Stikkel

Alexandros Nikou

Marios Daoutis

Rana Pratap Sircar

Harald Edquist

Jens Malmodin

Ahmet Cihat Baktir

A computer-implemented method performed in a multi-agent system by a first network node is provided for transferring historical data from an operating agent to a second agent for an action controlling a performance of the multi-agent system. The method includes selecting at least one operating agent for transfer of historical data to the second agent. The historical data acquired from executions of an action by the at least one operating agent that at least partially fulfills an input parameter. The selecting is based on one or more criteria including (i) a performance of the at least one operating agent on the parameter or on a related parameter; (ii) an availability of the at least one operating agent; and (iii) an identity of an actuation target system for receipt of the action. The method further includes transferring the historical data to the second agent.

Historical data transfer to agent in a multi-agent system for an action controlling performance of the multi-agent system

A method performed by a first network node (). The method is for handling charging of a wireless device (). The first network node () and the wireless device () operate in a wireless communications network (). The first network node () modulates () one or more beam forming beams () in an antenna array () controlled by the first network node () with a pulse width modulation. The first network node () then charges (), wirelessly, the wireless device (), with the modulated one or more beam forming beams ().

First network node, second node, wireless device and methods performed thereby for handling charging of the wireless device

A method of operating a protection node for protecting a pattern classification node from malicious requests may be provided. The protection node may receive, from a user node, a request containing an original pattern to be classified by a machine learning algorithm performed by the pattern classification node. The protection node may add noise to the original pattern to generate a noisy pattern. The protection node may obtain a first classification of the noisy pattern based on processing of the noisy pattern by a first clone of the machine learning algorithm at the protection node; obtain a second classification of the original pattern based forwarding the request for processing of the original pattern by the machine learning algorithm performed at the pattern classification node; and compare the first and second classifications to determine whether the first and second classifications satisfy a defined similarity rule. The protection node may use the comparison to manage the request from the user node.

Methods for protecting pattern classification node from malicious requests and related networks and nodes

A method and system for deployment of services for customers. Network service templates (NSTs) are determined, from a permissioned distributed ledger that is distributed between network operator systems of one or more network operators and customer systems. The NSTs include respective descriptions of network characteristics that provide respective logical networks. A subset of the NSTs and a corresponding subset of the network operator systems are selected based on a service description of the first service for deployment of the first service. Instantiation of the subset of the NSTs is requested on the corresponding subset of network operator systems as a subset of network slice instances to form the first service deployed for the first customer.

Network slice instance provisioning based on a permissioned distributed ledger

Techniques for distributed object detection and tracking are described. In an example method, a first current frame from a series of frames is sent to a first node, for detection of a first object. After object detection information for the first object is received in return, a second node is selected, and a second current frame is sent to the second node for an updated object detection. In addition, while waiting for the results of the updated object detection, two or more frames following the second current frame are sent to respective tracking nodes. Object modelling information indicating location and/or classification of one or more objects, as derived from the previously received object detection information is also sent to each of the respective tracking nodes. Tracking information for the first object is received from each of the respective tracking nodes.

Distributed computation for real-time object detection and tracking

A user device is configured to receive Hypertext Transfer Protocol, HTTP, requests sent by a user of the device. Information is obtained from the received HTTP requests about areas of interest to the user. The information obtained from the received HTTP requests is combined to generate a machine-generated representation of the areas of interest to the user. Then, data traffic sent to the user is received, and is modified based on the machine-generated representation of the areas of interest to the user.

Method of filtering data traffic sent to a user device

AM and LM parameters to be used for adapting an ASR model are derived for each audio segment of an audio stream comprising multiple audio programs. A set of identifiers, including a speaker identifier, a speaker domain identifier and a program domain identifier, is obtained for each audio segment. The set of identifiers are used to select most suitable AM and LM parameters for the particular audio segment. The embodiments enable provision of maximum constraints on the AMs and LMs and enable adaptation of the ASR model on the fly for audio streams of multiple audio programs, such as broadcast audio. This means that the embodiments enable selecting AM and LM parameters that are most suitable in terms of ASR performance for each audio segment.

ASR training and adaptation

Methods and devices enable connecting devices to cellular networks using the devices' hardware identifiers. Subscriber records include a hardware identifier assigned when the devices are manufactured. A target hardware identifier included in an attachment request is associated with an International Mobile Subscriber Identity, IMSI, available to the cellular network if, according to subscriber records, the device is registered.

Method and devices for hardware identifier-based subscription management

Embodiments herein relate to a method performed by a transmitting device () for transferring background user data to a receiving device () in a communication network (). The transmitting device () intercepts a packet, which packet comprises foreground user data in a payload field of the packet. The transmitting device () determines that the packet is intended for the receiving device (). The transmitting device () establishes an available amount of data in the payload field of the packet. The transmitting device () adds the background user data for the receiving device () to the established available amount of data in the payload field. The transmitting device () transmits the packet with the foreground user data and the background user data to the receiving device ().

Computer program, computer-readable storage medium transmitting device, receiving device and methods performed therein for transferring background user data

There is provided mechanisms for handling poor audio quality. A method is performed by a receiving terminal device. The method comprises obtaining an indication of poor audio quality of incoming audio at the receiving terminal device. The incoming audio originates from a transmitting terminal device. The method comprises initiating text conversion of the incoming audio. The method comprises receiving text resulting from automatic speech recognition having been applied to the incoming audio. The method comprises providing a representation of the text to a user interface of the receiving terminal device.

Handling of poor audio quality in a terminal device

Methods () and devices () enable tracking using existing mobile networks' infrastructure. A radio module (, R,) that is attached to a shipment sends one or more network attachment requests including a network-type identifier. Although the requests are rejected, the network records location information if the network-type identifier is actively associated with a shipment identifier.

Method and system for cellular-based shipment tracking

A method performed by a serving network node for handover of a communication device to a target network node is provided. The method includes determining, based on content of an Information Centric Networking, ICN, request, a need for handover, and initiating, in response to the determining, a handover of the communication device to the target network node. A method performed by a network entity is also provided. A network node, network entity, computer programs and computer program products are also provided.

Methods and network entities for handover

A method performed by a first peer node for acting as a P2P proxy for a user device is provided. The first peer node operates in a communications network. The first peer node establishes () a renewable energy profile of the first peer node used for powering the first peer node. The renewable energy profile indicates to what extent one or more energy sources are renewable. When a proportion of renewable energy according to the established renewable energy profile exceeds a first threshold, the first peer node acts () as a P2P proxy for the user device for downloading a piece of data to the user device by being powered according to its renewable energy profile.

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Sollentuna, Sweden

Maxim Teslenko