Abstract: In one aspect, network nodes maybe configured for improving predictability of application performance in a wireless communication system. A mobile network domain predicts an upcoming network event affecting a UE operating in the wireless communication system and notifies an application server (AS) providing an application service to the UE of the predicted network event. The AS may be notified through an Application Function (AF). The AS receives the notification and adjusts operation of the application service and/or a configuration of the UE, in response to the notification.

Abstract: There is provided mechanisms for performing an iterative learning process with agent entities. A method is performed by a server entity. The method comprises associating a secret key with a computational task. The secret key defines a transform to be used by the agent entities when reporting computational results of the computational task to the server entity. The transform has an inverse. The method comprises configuring the agent entities with the computational task. The method comprises performing the iterative learning process with the agent entities until a termination criterion is met. The server entity as part of performing the iterative learning process applies the inverse of the transform to the computational results received from the agent entities.

Abstract: A method (400) performed by a first network node (101). The method includes the first network node obtaining (s402) model performance feedback information (MPFI) for an artificial intelligence (AI) model. The method also includes the first network node providing (s404) the MPFI to a model training function (112). The MPFI provides information that the model training function (112) can use to determine whether the AI model needs to be updated or replaced by a new AI model.

Abstract: There is provided mechanisms for configuring agent entities with a reporting schedule for reporting computational results during an iterative learning process. A method is performed by a server entity. The method comprises configuring the agent entities with a computational task and a reporting schedule. The reporting schedule defines pairs of the agent entities. According to the reporting schedule and per each iteration of the learning process, each of the agent entities in each pair is to report its own computational result of the computational task to both the server entity and the other of the agent entities in the same pair. When reporting its own computational result to the server entity, each of the agent entities is to superimpose the computational result of the other of the agent entities in the same pair. The method comprises performing the iterative learning process with the agent entities until a termination criterion is met.

Abstract: Optimizing link adaptation for a communication session with a user device is disclosed herein. In one embodiment, a method performed by a network node of a telecommunications network to optimize link adaptation for a communication session with a user device comprises receiving a link adaptation state update message from a user device, wherein the link adaptation state update message comprises one or more link adaptation state information elements associated with a communication link between the network node and the user device. The method further comprises determining one or more link adaptation parameters for communicating with the user device based on the link adaptation state update message.

Abstract: There is provided mechanisms for configuring agent entities with a reporting schedule for reporting computational results during an iterative learning process. A method is performed by a server entity. The method comprises configuring the agent entities with a computational task and a reporting schedule. The reporting schedule defines an order according to which the agent entities are to report computational results of the computational task. The agent entities are configured to, per each iteration of the learning process, base their computation of the computational task on any computational result of the computational task received from any other of the agent entities prior to when the agent entities themselves are scheduled to report their own computational results for that iteration. The method comprises performing the iterative learning process with the agent entities according to the reporting schedule and until a termination criterion is met.

Abstract: A method is performed by a wireless device for communication in a non-terrestrial network, NTN, comprising at least a first non-terrestrial network node and a second non-terrestrial network node. The method includes determining an estimated location of the wireless device. The method further includes obtaining first location information associated with the first non-terrestrial network node and second location information associated with the second non-terrestrial network node. The method further includes calculating an estimated signal time arrival difference based on the location of the wireless device, the first location information, and the second location information. The method further includes using the estimated signal time arrival difference to provision an NTN-specific synchronization signal/physical broadcast channel, SS/PBCH, block measurement timing configuration, NTN-SMTC.

Abstract: Embodiments include methods for radio link monitoring (RLM) by a user equipment (UE) in a combined terrestrial and satellite wireless communication network. Such methods include, during a discontinuity in a connection between a UE and a terrestrial network node (TNN), performing RLM of the connection based on a second configuration. Such methods also include, before and/or after the discontinuity, performing RLM of the connection based on a first configuration. The connection includes a first link between the UE and a satellite and a second link between the satellite and the TNN. The discontinuity includes a link switch in at least one of the first link and the second link. The RLM before, during, and after the discontinuity can include various first, second, and third operations, respectively. Embodiments also include complementary methods performed by a TNN, as well as UEs and TNNs configured to perform operations corresponding to such methods.

Abstract: The present disclosure relates to a computer-implemented method (100), performed by a first network node, for provisioning a wireless device with prediction information for allowing the wireless device to predict a radio signal measurement between the wireless device and a base station, wherein the prediction information comprises a denoising autoencoder and at least one candidate noising pattern. The method comprises: receiving (110), from the wireless device, wireless device capability information: obtaining (120), based on the wireless device capability information, the denoising autoencoder and/or the at least one candidate noising pattern for predicting a radio signal measurement; and transmitting (130) an indication of the prediction information to the wireless device. The present disclosure also relates to a first network node, a wireless device and a computer program.

Abstract: A method performed by a first network node for enabling a first wireless device to obtain background RF signal energy from background RF signals provided by the first network node and/or at least one second network node in a wireless communications network on a set of time-frequency resources is presented. The first network node estimates a background RF signal energy available to the first wireless device in each time-frequency resource in the set of time-frequency resources. The first network node determines a first subset of the set of time-frequency resources that the first wireless device is to use for obtaining background RF signal energy based on the estimated background RF signal energy. The first network node transmits information indicating the determined first subset of the time-frequency resources to the first wireless device. A network node a first wireless device and method therein are also provided.

Abstract: Embodiments include methods for a radio access network (RAN) node. Such methods include transmitting downlink (DL) reference signals (RS) in accordance with one or more configurations and receiving, from each of one or more user equipment (UEs), feedback representing a DL channel from the RAN node to the UE. The feedback is based on the UE's measurements of the transmitted DL RS and is encoded by one or more UE encoders that correspond to respective RAN node decoders. Such methods include, for each of the one or more UEs, obtaining one or more measurements of uplink (UL) RS transmitted by the UE in accordance with the one or more configurations. Such methods include training the RAN node decoder(s) based on the received feedback and the obtained UL RS measurements. Other embodiments include complementary methods for UEs, as well as RAN nodes and UEs configured to perform such methods.

Abstract: Methods performed by network nodes in a radio access network may be provided. A first network node may receive an identification of data models supported by a second network node. Each of the identified data models may include a data model for calculating a compact representation of data collected, at least one correlation parameter, and an accuracy metric. The first network node may transmit a request that the second network node calculate an identified one of the data models. The first network node may receive the calculated data model from the second network node. The first network node may evaluate the received calculated data model based on determining whether the received calculated data model correlates to at least one correlation parameter.

Abstract: A method by a transmitting node for adapting a transmission mode based on a capability of a receiving node includes obtaining information indicating a capability of the receiving node to receive signals having a certain level of distortion. The transmitting node transmits a signal to the receiving node, the signal transmitted using a transmission mode selected based on the capability of the receiving node.

Abstract: In a first method, a wireless device estimates a delay profile of a channel impulse response, CIR, for a channel between a network node and the wireless device, compresses the delay profile using a compression function, and transmits the compressed delay profile. The compression function includes a first function and a quantizer. The first function is configured to receive input data and reduce a dimension of the input data. In a second method, a network node receives a compressed delay profile of CIR for a channel between a network node and a wireless device, decompresses the compressed delay profile using a decompression function, and estimates a position of the wireless device based on at least the decompressed delay profile. The decompression function includes a first function which is configured to receive input data and provide output data in a higher dimensional space than the input data.

Abstract: A system, method and non-transitory computer readable media for facilitating reduced signaling load with respect to UE location reporting in a non-terrestrial network (NTN) environment (100) interoperable with one or more terrestrial cellular communications networks. In one embodiment of an index-based location reporting scheme, a UE (105, 1200) may receive (402) a list of reference locations from a network node (1000, 1100), each identified with an index. Responsive to receiving the list of reference locations, the UE (105, 1200) may report to the network node (1000, 1100) an index or a subset of indices of one or more reference locations that match a specific reporting criterion. In an example implementation, reporting of index-based location information by the UE (105, 1200) may be configurably triggered, e.g., periodically or in response to the occurrence of a particular event, condition, etc.

Abstract: A method performed by a first network is provided. The method includes the first network node generating a first message, the first message comprising a request relating to an energy or power consumption status of a second network node. The method includes transmitting the first message towards the second network node. The method includes receiving a second message transmitted by the second network node, the second message comprising a response to the first message. A method performed by a second network node is also provided. The method includes the second network node receiving a first message transmitted by a first network node, the first message comprising a request relating to an energy or power consumption status of the second network node. The method includes the second network node transmitting a second message towards the first network node, the second message comprising a response to the first message.

Abstract: A method (600) performed by a first network node (100A, 800) for coordinating with a second network node (100B, 800) with respect to an energy metric. The method includes the first network node determining or identifying (s602) based on the energy metric at least one of: (i) an action to perform or (ii) a request relating to the second network node. The method also includes the first network node generating (s604) a first message, the first message comprising an indication that the action or the request is due to an energy saving reason. The method further includes the first network node transmitting (s606) the first message towards the second network node.

Abstract: Systems and methods are disclosed herein that relate to influencing training of a Machine Learning (ML) model based on a training policy provided by an actor node are disclosed herein. In one embodiment, a method performed by a first node for training a ML model comprises receiving a training policy for a ML model from a second node, the training policy comprising information that indicates two or more accuracy or importance metrics for two or more ranges of values for a variable to be predicted by the ML model. The method further comprises training the ML model based on a training dataset and the training policy. In one embodiment, the first node is either a training and inferring node or a training node that operates to train the ML model, and the second node is an actor node to which predictions made using the ML model are to be provided.

Abstract: The present disclosure relates to a method performed by a UE for enabling GNSS measurements. The UE and a network node are included in a NR communications network, and the NR communications network includes a NTN component. The UE performs a GNSS measurement according to a rule and using a GNSS receiver included in the UE. At least one of a UE position and an absolute time is an output of the GNSS measurement or is derivable from the GNSS measurement.

Abstract: Embodiments include methods for a first network node of a wireless network. Such methods include sending, to a second network node in the wireless network, a first message comprising an indication of predicted future adjustments of mobility-related settings, of the first network node, for mobility of users between a coverage area of the first network node and an adjacent coverage area of the second network node. Such methods include receiving, from the second network node in response to the first message, a second message indicating one or more of the following: acknowledgement, confirmation, or rejection of the predicted future adjustments; and corresponding adjustments of mobility-related settings for the second network node. Other embodiments include complementary methods for a second network node and a third network node, as well as network nodes configured to perform such methods.