Telefonaktiebolaget Lm Ericsson (publ)

Sago Strategic Solutions LLC

Konstantinos Vandikas

Athanasios Karapantelakis

Maxim Teslenko

Rafia Inam

Ioannis Fikouras

Leonid Mokrushin

Aneta Vulgarakis Feljan

Qiang Li

Lackis Eleftheriadis

Keven Wang

Volodya Grancharov

Vlasios Tsiatsis

Christofer Flinta

Elena Fersman

Joerg Niemoeller

Hongxin Liang

Roman Levenshteyn

Sigurdur Sverrisson

Erlendur Karlsson

Yifei Jin

Valentin Kulyk

Eugen Freiter

Abhishek Sarkar

Amos Kao

Catalin Meirosu

Ioanna Pappa

Abdulrahman Alabbasi

Nicolas Seyvet

Selim Ickin

Sibel Tombaz

Hjalmar Olsson

Patrik Wiss

Junaid Shaikh

Calin Curescu

Jawwad Ahmed

Daniel Lindström

Sébastien Pierrel

Anna Viggedal

Marin Orlic

Neiva Lindqvist

Adam Bergkvist

Gábor Stikkel

Klaus Raizer

Craig Donovan

Farjola Peco

Christian Schaefer

Roman Zhohov

Karl Viktor Berggren

Dimitri Mazmanov

Nina Washington

Paris Carbone

Michael Buisman

Dmitri Mazmanov

Manfred Dasselaar

Erik Sanders

A testing system and method for testing a Printed Circuit Boards (PCBs) is provided. The method is being executed at a testing system which comprises a RF test analyzer; a RF energy source; and one or more RF probes. The method includes performing a first level scanning of a first set of components in the PCB. The method further includes performing a second level scanning of another set of components in the PCB, which differs from the first set of components in the PCB; the second level scanning is performed only if anomalies are identified which is based on analyzing the results of the performed first level scanning. The method further includes determining detailed root causes of the identified anomalies which is based on analyzing results of the performed second level scanning.

Method and system for testing a printed circuit board

A network node for handling operation of the network node in a communication network. The network node includes at least one power supply unit and one or more additional power units for supplying power to the network node. The network node is configured to obtain an output from a computational model. The network node is configured to set an output voltage from the at least one power supply unit based on the obtained output.

Power management using rectifier circuits TN wireless networks

A method by a first wireless device, for determining a status of a first cell is described herein. The first wireless device operates in a communications network. The first wireless device sends a request to a plurality of second wireless devices operating in the communications network. The plurality of second wireless devices is within radio range. The request inquires whether or not the first cell is in sleeping state. The first wireless device receives, in response to the sent request, one or more responses from the plurality of second wireless devices. The first wireless device determines whether or not the first cell is in sleeping state, based on the received responses. The methods by a network node and another wireless device receiving an indication indicating a result of the determination are also described.

First wireless device, first network node, second wireless device, and methods performed thereby, for determining a status of a cell

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

Federated learning is used to predict network communication performance at an edge node (-). The edge node (-) trains a local model (-) of network communication performance over one or more rounds of training, based on a local training dataset (-) and based on multi-node training information (-) received in each round of training. This information (-) comprises information about local models (--N) at other respective edge nodes (--N) as trained based on local training datasets at the other edge nodes (--N). After or as part of each round of training, the edge node (-) transmits control signaling that indicates an accuracy of the local model (-) as trained through that round of training, that indicates whether another round of training is needed or desired, and/or that indicates whether any further information (-) is needed or desired. The edge node (-) predicts network communication performance at the edge node (-) based on the trained local model (-).

Predicting network communication performance using federated learning

A method for managing allocation of network resources at a site in a communication network is disclosed. The method is performed by a resource manager and comprises receiving a notification of an event occurring at a network entity, the network entity being associated with the site and with a network operator. The event comprises at least one of a performance event, a fault event or an update event. The method further comprises determining an implication of the notified event for traffic flow in the network and determining, on the basis of the implication of the notified event for traffic flow in the network, an operational requirement to compensate for the event. The method further comprises obtaining configuration information for fulfilment of the operational requirement and instructing an infrastructure manager for the site to allocate network resources in accordance with the obtained configuration information in order to satisfy the operational requirement.

Method and apparatus for network resource allocation

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

An operator system of a wireless communication network operator sends, to a regulator system of a regulator, a record that includes information about administration of a subscription identifier associated with the wireless communication network operator. Responsive to sending the record to the regulator system, the operator system receives a response that indicates whether the regulator system approves of or rejects the record being added to a permissioned distributed database that is distributed at least in part between the regulator system and the operator system. The operator system adds or does not add the record to the permissioned distributed database depending on the response.

Administration of subscription identifiers in a wireless communication network

A method by a WiFi AP for setting-up a WiFi connection with a wireless device, includes sending WiFi service credentials to a Light Fidelity (Li-Fi) AP for transmission through Li-Fi signaling that is broadcast for reception by wireless devices. The method receives and authenticates an authentication request that is received via a RF transceiver of the WiFi AP from the wireless device, which is responding to the WiFi service credentials that were broadcast through the Li-Fi signaling. The method then establishes a WiFi RF connection with the wireless device responsive to the authentication.

Establishing connections between WiFi access points and wireless devices via light fidelity access points

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

Methods and devices optimize backup power management for network devices connected to backup power sources including two different batteries. Machine learning techniques are used to predict upcoming power outages affecting the network device based on power-related historic information and current conditions. A backup-power operation plan prescribing usage of the backup power sources during the predicted power outages is then generated. The backup-power operation plan schedules a battery among the backup power devices to be used at least twice without being recharged.

Method and devices for optimizing backup power control using machine learning

Embodiments described herein provided methods and apparatus for controlling switching a power supply of a first network node between an electrical power grid and an alternative power supply in which the first network node is configured to transmit data to a second network node over a data transmission link. The method includes receiving first information indicative of a predicted decrease in a transmission rate of data transmitted by the first network node over the data transmission link. Responsive to receiving the first information, a first request is transmitted to the first network node to switch the power supply of the first network node from the electrical power grid to the alternative power supply.

Methods and apparatus for controlling the power supply of a network node

Method and system for Root Cause Analysis (RCA) across multiple network systems. Update information of a first local root cause analysis mechanism is received. An RCA controller generates, based on the update information, a new node to be added to a global root cause decision tree, where the global root cause decision tree is to be shared by at least two of the plurality of network operators. The RCA controller requests storage of the new node in a distributed ledger that is shared by network operators. The RCA controller participates in a verification operation of the new node. In response to determining that the verification operation is successful, the RCA controller adds an entry including the new node to the distributed ledger as part of the global root cause decision tree. Alternatively, when the verification operation is not successful, the new node is not added to the distributed ledger.

Method and system for root cause analysis across multiple network systems

Growing community of inventors

Solna, Sweden

Konstantinos Vandikas