Abstract: A computer-implemented method performed by a computing device determine a plurality of frequency periodicities for decision making for an operation of at least one service in a communication network is provided. The method includes determining a first frequency periodicity for a first decision variable for the operation of the at least one service. The method further includes determining a second frequency periodicity for a second decision variable for the operation of the at least one service. The first frequency periodicity is different than the second frequency periodicity. The method further includes outputting the first frequency periodicity for the first decision variable and the second frequency periodicity for the second decision variable. Related methods and apparatus are also provided.

Abstract: A ML method for beam selection in a communication network. The method comprises transmitting, by the first network node to the controller, a first network node data set and transmitting, by the second network node to the controller, a second network node data. The method further comprises training, by the controller, a ML model using the first network node data set and the second network node data set in conjunction and generating, by the controller, a policy using the ML model based on a global reward function, The method further comprises transmitting, by the controller to the first network node and the second network node, the policy and controlling by the first and second network nodes beam selection based on the policy.

Abstract: Embodiments described herein relate to methods and apparatuses for enabling sensing of an object. A computer-implemented method in a target network node comprises: receiving a request from a source network node currently performing sensing of the object to handover sensing of the object to the target network node; obtaining an indication of a location of the object; determining, based on the indication of the location, whether the target network node can perform sensing of the object; and responsive to determining that the target network node can perform sensing of the object, starting sensing of the object.

Abstract: According to an aspect, there is provided a computer-implemented method performed by a first network node in a communication network. The method comprises using a Machine Learning, ML model to predict a timing of one or more analytics requests by a second network node to the first network node. An analytics request is a request by the second network node for an analytics report. The method further comprises, before the predicted timing of a first analytics request, obtaining data relating to the first analytics request from one or more data sources; generating a first analytics report relating to the first analytics request by analysing the obtained data; and sending the generated first analytics report to the second network node prior to the predicted timing of the first analytics request.

Abstract: According to an aspect, there is provided a method performed by a network node (10; 14) in a communication network. The method comprises generating (501), using a first trained model, respective analytics reports for a plurality of requests received from a plurality of network function, NF, consumers (12). Each generated analytics report comprises an indication of an action that the respective NF consumer is predicted to take in response to evaluating the respective analytics report. The method further comprises determining (502) one of the requests to prioritise; and sending (503) the generated analytics report corresponding to the prioritised request to the NF consumer that the prioritised request was received from.

Abstract: A computer implemented method performed by a first network node in a communications network for training a first agent to select an action in order to perform a task according to a Reinforcement Learning, RL, process. The method comprises: i) determining a first action using the first agent, based on a first state in the communications network, ii) sending the first action and the first state to a second network node having a second agent, iii) receiving from the second network node, feedback based on output from the second agent, on taking the first action when the communications network is in the first state, and iv) using the feedback in the training of the first agent.

Abstract: A computer implemented method for managing QoS of wireless devices in a communication network is disclosed. The method includes receiving a QoS intent from a first node, the QoS intent including an identification of a wireless device to which the QoS intent applies and a QoS requirement for the identified wireless device. The method further includes obtaining a specification of available QoS in the communication network and a specification of QoS policies in the communication network, using an ML model to determine, based on the received QoS intent and obtained specifications, at what time the at least one QoS requirement of the QoS intent can be fulfilled for the identified wireless device, and informing the first node of a result of the determination. Also disclosed is a QoS management node.

Abstract: According to an aspect, there is provided a computer-implemented method performed by a network node in a communication network. The method comprises: forming one or more clusters of wireless devices that are attached to the network node, wherein each cluster comprises two or more wireless devices; for a first cluster, transmitting, to a first wireless device in the first cluster, a request for objects in an environment in which the two or more wireless devices are located to be sensed; and receiving, from a wireless device, information relating to sensed objects.

Abstract: A method and system for sharing radio network resources in a distributed ledger (DL) network that is operated between a plurality of participants including a first participant and a second participant. A DL node of the second participant determines from a distributed ledger of the DL network a smart contract that allows sharing of radio network resources owned by the first participant; executes the smart contract to generate one or more transactions between the first participant and the second participant, where a first transaction of the transactions indicates that a first portion of the radio network resources of the first participant is shared with the second participant; and records, through a consensus mechanism, the transactions in the distributed ledger.

Abstract: Embodiments herein disclose a method for facilitating a privacy-aware representation in a system. The method comprises determining using a content analyzer a layout of a scene. Thereafter, one or more objects in the scene is identified using the content analyzer to define a relationship between the objects. A privacy status to be tagged to the one or more identified objects is inferred by using a machine learning model. At least one object is processed based on the privacy status inferred. A privacy-aware representation of the scene is rendered, wherein the privacy-aware representation displays the scene with at least one processed object.

Abstract: A method performed by a first network node is provided. The first network node includes a decoder of a first autoencoder. The method comprises receiving first encoded data. The first encoded data was transmitted by a second network node, and the first encoded data was generated using an encoder of a second autoencoder. The encoder of the second autoencoder is included in the second network node. The method also comprises converting the first encoded data into first converted-encoded data, thereby mapping output of the encoder of the second autoencoder to output of the encoder of the first autoencoder; and decoding the first converted-encoded data using the decoder of the first autoencoder. The first autoencoder and the second autoencoder are different.

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: A wireless device (10) measures Channel State Information, CSI, data for a wireless channel between the wireless device (10) and a node (100) of the wireless communication network. Further, the wireless device encodes the CSI data by an encoder part of an machine-learning based autoencoder. Further, the wireless device (10) applies a mask to the encoded CSI data. The mask defines unmasked data elements and masked elements of the encoded CSI data. The wireless device (10) then transmits the encoded CSI data excluding the masked data elements to the node (100).

Abstract: A method performed by a control node (800) for determining whether two wireless devices meet a location criterion for closeness, the method comprising: obtaining (701), from the two wireless devices, measurements of a reference signal transmitted in a beam by a radio network node to a plurality of measurement points that are spatially distributed around the two wireless devices; and determining (702) whether the two wireless devices meet the location criterion based on the obtained measurements.

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.

Abstract: A method for providing frequency balancing in a power network grid by an RBS of a radio communication network. The RBS includes one or more power supply units, PSUs, connected to the power network grid. The method includes detecting a deviation of a power network grid frequency measured in a PSU of the one or more PSUs, the power network grid frequency is measured upstream of a power factor correction, PFC, unit of the PSU, deactivating the one or more PSU in response to the detected deviation, determining one or more further PSU to be deactivated based on the detected deviation, the RBS is in a first frequency containment reserve, FCR, zone and the one or more further PSU are in a second FCR zone, other than the first FCR zone, and sending a deactivation indication to the determined one or more further PSU.

Abstract: Methods, nodes, computer programs and communication networks are disclosed. The disclosure provides a method performed by a network node for controlling traffic in a communications network, the communications network comprising a wireless access node adapted to serve a plurality of wireless devices. The method comprises obtaining network operation data for at least one of the plurality of wireless devices, the network operation data comprising location of the respective wireless device and performance data correlated with the location, the network operation data being indicative of a plurality of attributes corresponding to the operation of the respective wireless device in the communications network. The method also comprises grouping the at least one wireless device of the plurality of wireless devices by partitioning the network operation data into one or more service clusters wherein each service cluster defines a geographical area based on the obtained location.

Abstract: A method for enabling a model trainer to train a model. The method includes transmitting a first certificate request message to an endpoint associated with a first secure enclave. The method also includes receiving a first certificate response message responsive to the first certificate request message, wherein the first certificate response message comprises a first certificate generated by the first secure enclave and a first digital signature generated by the first secure enclave for authenticating the first certificate. The method also includes determining whether the first certificate is valid. The method also includes, as a result of determining that the first certificate is valid, transmitting to the endpoint or to the model trainer a model information message comprising information pertaining to the model.

Abstract: A technique for detecting a fake radio base station, RBS, in a radio access network, RAN, comprising a plurality of RBSs is described. As to a method aspect, a neural network in an FRD module is trained according to reinforcement learning with a set of experiences. Each of the experiences relates to one of the RBSs and includes a state based on at least one observation of at least one radio device relative to the respective one of the RBSs, an action indicative of a degree of trust whether the respective one of the RBSs is a fake RBS, an updated state for the respective one of the RBSs, and a reward based on a likelihood function. The reward is indicative of a correlation between the action and the likelihood function for the respective one of the RBSs being a fake RBS based on the respective one of the states.

Abstract: Systems and methods are described for the transfer learning for digital twins. Embodiments can include a variety of machine learning, reinforcement learning, transfer learning, and other embodiments. Training digital twins of physical objects can run into the problem of limited access to data transfers. In embodiments under the present disclosure a first training can be performed to determine a source domain or transfer learning method that is best, given a certain state or other metadata of a digital twin. Further training can utilize source domains that previously performed best given state and/or metadata and digital twin type.