Abstract: Methods and systems for power supply unit (PSU) control. A method includes measuring one or more properties of the PSU to obtain property measurements, and initiating transmission of the property measurements to a machine learning (ML) agent hosting a trained ML model. The method further includes receiving the property measurements at the ML agent, and processing the received property measurements using the trained ML model to generate suggested actions to be taken by the PSU. The method further includes predicting the effect of each of the suggested actions on the measured PSU properties, and selecting a subset of the suggested actions predicted to have a significant impact on the measured PSU properties. The method further includes initiating transmission of the selected subset of suggested actions to the PSU, and performing, at the PSU, the selected subset of suggested actions.

Abstract: Decision-making equipment (22) is configured for link change decision-making using reinforcement learning. The decision-making equipment (22) is configured to track rewards (30-1, . . . 30-M) earned for, and outcomes (28-1, . . . 28-M) of, respective link change decisions (26-1, . . . 26-M). In some embodiments, possible outcomes of a link change decision to change a serving link of a wireless device to a target link include at least: a change of the serving link of the wireless device from the target link to another link; and a network-initiated disconnect of the wireless device from the target link. Regardless, the decision-making equipment (22) is also configured to make a link change decision (28-(M+1)) based on the tracked rewards (30-1, . . . 30-M) and outcomes (28-1, . . . 28-M).

Abstract: There is provided a method performed by a central entity of a network. A first set of features is selected for a machine learning model to take into account when analysing data. The machine learning model is to be deployed at an edge entity of the network. The selection is based on first information indicative of data that is available for the machine learning model to analyse, second information indicative of features that are available for the machine learning model to take into account when analysing data, and contextual information associated with the network.

Abstract: A method is provided for determining bias of machine learning models. The method includes: forming a training dataset including input data samples provided to a remote machine learning model developed using a machine learning process, and corresponding output data samples obtained from the remote machine learning model; training a local machine learning model which approximates the remote machine learning model using a machine learning process and the training dataset; and interrogating the trained local machine learning model to determine whether the remote machine learning model is biased with respect to one or more biasing data parameters.

Abstract: This disclosure provides new parameters for discovery request messages and discovery response messages communicated between a Network Function (NF) consumer (an NFc) (100) and a Network Repository Function (NRF) (130) in a core network. The new parameters support the discovery and selection of one or more NF services to be consumed by the NFc. More particularly, when the NFc initiates a discovery request, it identifies a particular NF service type to consume to the NRF. Included in the discovery request message are the new parameters specifying a particular set of one or more quality metrics the NRF is to report to the NFc in the discovery response message. The quality metrics include the most current measurement values for the specified NF service, but may also include historical measurement values spanning a time duration that is specified by the NFc in the discovery request message.

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: 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.

Abstract: 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.

Abstract: Embodiments herein disclose e.g. a method performed by a radio network node for controlling power provision of one or more antenna elements associated with a user equipment. The radio network node obtains an indication of a capability relating to an output power of the user equipment; and a state indication of the user equipment indicating an activity level of the user equipment. The radio network node further controls a power arrangement controlling power provision to the one or more antenna elements, based on the state indication and/or the indication of the capability.

Abstract: A coordination node (1000) is disclosed for coordinating generation of an accident information graph comprising information representative of a vehicle accident environment. The coordination node comprises processing circuitry (1010) configured to detect an event associated with occurrence of a vehicle accident (110) and obtain environment data representative of the vehicle accident environment in which the event was detected (120). The processing circuitry is further configured to generate an accident information graph based on the obtained environment data, wherein the accident information graph comprises a structured representation of the obtained environment data and at least one semantic annotation to the obtained environment data (130).

Abstract: 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.

Abstract: Mechanisms for migration of a stateless computation service in a mobile communication network. A method, performed by a control node comprises obtaining, from a core network node in the mobile communication network, information indicative of a need to migrate the stateless computation service from a source compute node in the mobile communication network. The need results from resource utilization at the source compute node is too high, and/or a demand from user equipment for the stateless computation service elsewhere in the mobile communication network. The method comprises initiating migration of the stateless computation service from the source compute node to a target compute node in the mobile communication network for hosting of the stateless computation service at the target compute node.

Abstract: 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.

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 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: 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 (14, 20) for enabling mitigation of radio traffic overload in at least one radio cell (19) among a group of radio cells (17, 18, 19). In an aspect, a method of a supervising device (20) of enabling mitigation of radio traffic overload in at least one radio cell (19) among a group of radio cells (17, 18, 19) is provided.

Abstract: 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.

Abstract: A reinforcement learning (RL) process is used to allocate UL/DL resources and is also used to offload traffic from a first access point (e.g., macro access point) to a second access point (e.g., a micro access point) when the load on the first access point is too high. The RL process is able to handle the dynamic nature of UL/ DL imbalances and is therefore able to maximize usage of resources without compromising quality of service.