Abstract: A computer-implemented method is provided performed by a first computing device for causal encoding of channel state information, CSI, is provided. The method includes obtaining an indication of incorrect reporting of CSI; retrieving parameter data; obtaining a measurement of CSI data; and activating a machine learning, ML, model for CSI reporting. The ML model including causal relationships among the parameter data and the measurement of CSI data. The method further includes encoding the measurement of CSI data to obtain encoded CSI measurement data; applying the ML model to the parameter data and the encoded CSI measurement data to obtain an adapted CSI measurement data; and transmitting the adapted CSI measurement data to a second computing device. A computer implemented, method performed by a second computing device, and related methods and apparatus are also provided.

Abstract: A method for handling compression of traffic. A first network node (101) obtains (201) respective first information from one or more radio network nodes (110). The radio network nodes (110) have access to a core network (120) via a respective link (140) to a second network node (102). The respective first information indicates: i) one or more respective indications of a status of the radio network nodes (110), and ii) one or more respective characteristics of a respective traffic between the radio network nodes (110) and the second network node (102). The first network node (101) then determines (203), based on the respective first information, a respective type of compression and/or decompression to be applied to the traffic in the respective link (141) between at least a first radio network node (111) and the second network node (102). The first network node (101) then initiates (204) application of the compression and/or decompression of the determined type. Publ.

Abstract: A method for managing operational temperature conditions of one or more base stations being executed at a central computing device in a communication system is provided. The method includes acquiring initial operational parameters for each local computing device of a first group of local computing devices associated with the one or more base stations. The method further includes determining one or more clusters by grouping each local computing device of the first group of local computing devices under one of the determined clusters based on the acquired operational parameters. The method further includes training, a central machine learning (ML) model for each determined cluster. The method further includes acquiring, for each local computing device of a second group of local computing devices associated with the one or more base stations, updated operational parameters.

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: For a set of access nodes of a wireless communication network, data throughput of user plane, UP, traffic and control plane, CP, traffic generated by wireless communication devices being served by the access nodes on a plurality of wireless carrier signals is predicted. Further, mobility of the wireless communication devices is predicted. Further, based on the predicted data throughput and the predicted mobility, it is decided for at least some of the plurality of wireless carrier signals whether the wireless carrier signal is assigned either to serving UP traffic, to serving CP traffic, or to serving both UP traffic and CP traffic.

Abstract: A method to transfer a digital twin of a first network to a second network. The method comprises obtaining, for each router of the first network, a location of the router, mobility data of wireless devices attached to the router, and data traffic information of devices attached to the router. It further comprises computing a digital fingerprint of the first network on the basis of the router location, mobility data and data traffic information for all routers and comparing the digital fingerprint of the first network with a digital fingerprint of the second network, so that a transfer of the digital twin of the first network to the second network may take place on the basis of the comparison. Further, there are related methods, network nodes, computer programs, and computer program products.

Abstract: Methods and apparatuses for improving a performance of a network. A method in a first network function comprises obtaining, at an initial time to, a prediction of a performance degradation associated with at least one performance metric at a first time, t0+T, in the future; and responsive to obtaining the prediction, at each current time, t, from t0 to t0+T: selecting a first set of network functions, NFs, fi, wherein each network function in the first set is associated with a delay time, d(xi,t) for the NF to impact the network, wherein for each of the first set of NFs a first condition of d(xi,t)<t0+T?t is met; and transmitting an indication of the prediction to the first set of NFs.

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: 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: 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: 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).