Abstract: The present disclosure relates to a method of scheduling communication of a wireless communication device in a communications network, and a device performing the method. A method of a device of scheduling communication of a wireless communication device in a communications network is provided. The method includes determining a geographical path along which the wireless communication device is expected to travel, acquiring data indicating power availability of access points via which the wireless communication device is capable of communicating along the expected geographical path, determining, from the acquired data indicating power availability, via which of the access points communication is to be established along the expected geographical path for avoiding establishing communication with one or more of the access points indicated to be subjected to power failure, and scheduling the communication to occur via the determined access points.

Abstract: A cluster computing device is defined, associated with a plurality of network nodes of the communications network, and it generates a performance model relating configuration parameters of a network node to at least one energy performance parameter of the network node. The cluster computing device transfers the generated performance model to the plurality of network nodes, and then receives information from network nodes, indicating configuration parameters and at least one energy performance parameter of the respective network node. When the cluster computing device identifies a first network node as having a poor performance, it also identifies a second network node as having a good performance, and it causes the first network node to operate with cloned configuration parameters of the second network node.

Abstract: 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.

Abstract: There is provided a method for handling training of a machine learning model. The method is performed by a coordinating entity that is operable to coordinate the training of the machine learning model at one or more network nodes. In response to receiving a request to train the machine learning model, a first network node is selected (402), from a plurality of network nodes, to train the machine learning model based on information indicative of a performance of each of the plurality of network nodes and/or information indicative of a quality of a network connection to each of the plurality of network nodes. Transmission of the machine learning model is initiated (404) towards the first network node for the first network node to train the machine learning model.

Abstract: Methods and sewer nodes generate machine learning models using models trained locally while avoiding misinformation by selectively aggregating models trained locally using data stored in client devices, which are connected to the server node via a communication network. The client devices receive an initial model and return updated model parameters of a respective model locally trained. Logical explanations are obtained, for each of the client devices, based on the updated model parameters and at least one set of input and corresponding output values. A distance based on the logical explanations, for each client device in a secondary cluster, measures a deviation of the respective model relative to model(s) of client devices in a primary cluster. The output model is generated by selectively aggregating at least the models received from the client devices in the primary cluster, while assessing each client device in the secondary cluster based on the distance thereof.

Abstract: According to an aspect, there is provided a power supply unit, PSU, configured for use in a first radio access network, RAN, node in a communication network. The PSU includes a power supply input interface configured to enable the PSU to be connected to, and receive electrical power from, an electrical power supply; and a power line communication, PLC, unit connected to the power supply input interface and configured to connect to a first baseband, BB, processing unit in the first RAN node. The first BB processing unit is configured for processing baseband signals in the first RAN node. The PLC unit is configured to operate according to a PLC protocol to enable communications between the first BB processing unit and a second BB processing unit in a second RAN node that is also connected to the electrical power supply.

Abstract: The present disclosure relates to methods and devices (101, 106) of controlling reactive power of a power grid. In an aspect, a method of a radio base station (101) of controlling reactive power of a power grid is provided. The method comprises measuring (S101) an electrical property indicating a level of the reactive power supplied by the power grid to which the radio base station (101) is connected, and performing (S102) an action to stabilize the level of the reactive power of the power grid upon the measured electrical property reaching a certain value.

Abstract: 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.

Abstract: A method performed by a first node in a distributed network is provided. The method includes receiving a request from a second node for transfer learning of a machine learning model for a use case. The request includes a description of a local environment and a use case identifier at the second node. The method further includes identifying a matching criteria for the local environment of the second node based on the use case. The method further includes determining whether at least one distributed node from a plurality of distributed nodes in the distributed network satisfies at least one of a match or a closest match to the matching criteria.

Abstract: A method of operating a radio network node to adjust power consumption of a telecommunications network is provided. The method includes determining a traffic prediction representing how each node of a set of nodes will interact with the radio network node over a period of time using a combined traffic model based on a traffic model of each node in the set of nodes. The method further includes determining to enable or disable at least one power related feature of the radio network node based on the traffic prediction.

Abstract: According to an aspect, there is provided a method of operating an analysis node. The method comprises receiving, from a network node in a first communication network to which a first wireless device is attached, behaviour information relating to the behaviour of the first wireless device with respect to the first communication network during a time period following attachment of the first wireless device to the first communication network; analysing the received behaviour information with reference to predetermined behaviour information for wireless devices owned by one or more third parties to determine whether the first wireless device is owned by one of the third parties; and sending an indication of whether the first wireless device is to be authenticated in the first communication network, wherein the indication is based on whether the first wireless device is determined to be owned by one of the third parties.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments herein disclose e.g. a method performed by a network node (12) for handling operation in a communication network, wherein the network node comprises at least one power supply unit, and one or more additional power units for supplying power to the network node. The network node (12) obtains an output from a computational model; and sets an output voltage from the at least one power supply unit based on the obtained output.

Abstract: Methods, apparatus and systems for pre-fault detection of hardware degradation; when embodied in a radio base station (RBS) of a wireless communications network, the method is characterized by: sensing at least a portion of an electrical waveform at a circuit location in the RBS; determining the radio resource load of the RBS proximate to the time of sensing the electrical waveform, the radio resource load being a measure of scheduled radio transmissions; determining whether the sensed electrical waveform is within a nominal operating window corresponding to the radio resource load, wherein the nominal operating window is a function of the sensed electrical waveform for the radio resource load at a prior time; and, if the sensed electrical waveform is not within the corresponding nominal operating window, triggering an alarm to indicate that the hardware has reached a degradation limit.

Abstract: A cluster computing device is defined, associated with a plurality of network nodes of the communications network, and it generates a performance model relating configuration parameters of a network node to at least one energy performance parameter of the network node. The cluster computing device transfers the generated performance model to the plurality of network nodes, and then receives information from network nodes, indicating configuration parameters and at least one energy performance parameter of the respective network node. When the cluster computing device identifies a first network node as having a poor performance, it also identifies a second network node as having a good performance, and it causes the first network node to operate with cloned configuration parameters of the second network node.

Abstract: A system in a wireless network comprising a tower, a first radio unit, a power supply unit, a supplementary power source and a power control unit arranged to supply electric power to the first radio unit. The power control unit is operative to obtain a scheduled power demand related to an amount of electric power required in the first radio unit for transmissions scheduled in an imminent time interval as a resource block or subframe. The power control unit is further operative to supply electric power to the first radio unit from the power supply unit if the power demand does not exceed a power threshold, or from the power supply unit and the supplementary power source if the power demand exceeds the power threshold.

Abstract: An automated improving of quality of service of a data center. Transients of a power grid fed to a power supply unit are monitored by a probe. Information on transients is provided across an interface to a server of the data center. Based on characteristics of the transients, a reliability of the data center is subjected to automated updating. A request for migration of workload requiring a higher reliability than the updated reliability can be sent to a central management. When the central management has identified another data center that can meet the required reliability, the central management migrates or relocates the workload to the another data center.

Abstract: A method performed by network node for determining application of at least one machine learning model from a plurality of machine learning models in a multi-vendor communications network is provided. The network node can receive a request from an actor device operating in a target network to enable running a task for the target network by using a machine learning models from the plurality of machine learning models to perform the task. Responsive to the request, the network node can determine whether a machine learning model from the plurality of machine learning models can perform the task or can be translated to perform the task. Responsive to the determination, the network node can send a communication to the actor device. The communication can include information that a machine learning model is ready to perform the task or that no machine learning model was found to perform the task.

Abstract: A method performed by a first network node (111). The method is for handling charging of a wireless device (130). The first network node (111) and the wireless device (130) operate in a wireless communications network (100). The first network node (111) modulates (307) one or more beam forming beams (121) in an antenna array (124) controlled by the first network node (111) with a pulse width modulation. The first network node (111) then charges (308), wirelessly, the wireless device (130), with the modulated one or more beam forming beams (121).