Abstract: A method including: receiving, by a multi-panel user equipment from a serving network entity, information indicative of predicted transmissions of reference signals by a serving network entity or target network entity; and applying a panel prioritization strategy for the multi-panel user equipment based on the received information indicative of predicted transmissions of reference signals.

Abstract: Systems, methods, apparatuses, and computer program products for survival time dependent flexible handover execution are provided. One method may include providing, by a network node, at least one handover execution condition to at least one user equipment. The at least one handover execution condition may include one or more delta values with respect to one or more actual handover execution conditions to allow for early and/or late execution of a handover by the at least one user equipment. The method may also include indicating, to the at least one user equipment, a transfer interval and/or a survival time information for which the at least one handover execution condition is taken into account.

Abstract: According to an example embodiment, a client device is configured to receive a validation model from a centralised unit device, wherein the validation model includes a machine learning model configured to predict an output from an input based on a plurality of model parameters; collect radio measurements corresponding to the input of the validation model and parameters corresponding to the output of the validation model; obtain predicted parameters as the output of the validation model by feeding the collected radio measurements as the input into the validation model; compare the collected parameters and the predicted parameters; compute a plurality of gradient vectors for the plurality of model parameters of the validation model based on the comparison between the collected parameters and the predicted parameters; and transmit the plurality of gradient vectors for the plurality of model parameters of the validation model to the centralised unit device.

Abstract: Cognitive functions in a network, each comprising a learning module adapted to observe characteristics of the network and an inference module adapted to conclude, from the characteristics observed by the learning module, on a behavior of the network resulting from the observed characteristics, are controlled by halting and/or restarting operation of the learning module of a cognitive function of the cognitive functions, where operation of the inference module of the cognitive function is controlled independently from the halting and/or restarting the operation of the learning module of the cognitive function.

Abstract: This document discloses a solution for performing exploration of radio resource management actions. According to an aspect, a method in a terminal device includes: receiving configuration information from a network node of a radio access network; entering, in response to the configuration information and in a state of not having a need to transfer data, an exploration mode where a reduced set of radio connection functions are enabled compared with a default operating mode; triggering, in the exploration mode, an explorative handover from a source cell managed by the network node to a target cell; establishing a radio connection with a target network node as a result of the explorative handover to the target cell managed by the target network node; acquiring measurement data of the radio connection and transmitting the measurement data to the target network node.

Abstract: Various example embodiments for supporting communications management may be configured to support communications management for an operations technology (OT) domain that uses an associated network domain for supporting communications of the OT domain. Various example embodiments for supporting communications management for an OT domain that uses an associated network domain for supporting communications of the OT domain may be configured to support such communications management based on slice management capabilities. Various example embodiments for supporting communications management for an OT domain that uses an associated network domain for supporting communications of the OT domain, based on slice management capabilities, may be configured to support such communications management based on support for slice configuration across the OT domain and the network domain.

Abstract: There are provided measures for trust related management of artificial intelligence or machine learning pipelines in relation to the trustworthiness factor “explainability”. Such measures exemplarily comprise, at a first network entity managing artificial intelligence or machine learning trustworthiness in a network, transmitting a first artificial intelligence or machine learning trustworthiness related message towards a second network entity managing artificial intelligence or machine learning trustworthiness in an artificial intelligence or machine learning pipeline in said network, and receiving a second artificial intelligence or machine learning trustworthiness related message from said second network entity.

Abstract: There are provided measures for trust related management of artificial intelligence or machine learning pipelines. Such measures exemplarily include, at a first network entity managing artificial intelligence or machine learning trustworthiness in a network, transmitting a first artificial intelligence or machine learning trustworthiness related message towards a second network entity managing artificial intelligence or machine learning trustworthiness in an artificial intelligence or machine learning pipeline in the network, and receiving a second artificial intelligence or machine learning trustworthiness related message from the second network entity, where the first artificial intelligence or machine learning trustworthiness related message includes at least one criterion related to an artificial intelligence or machine learning trustworthiness aspect.

Abstract: There is provided an apparatus comprising means for: configuring a measurement collection for one or more pre-determined optimization problems at a first data producer; transmitting a first measurement request to the first data producer, wherein the first measurement request is indicative of requested measurement(s) for the one or more pre-determined optimization problems, time sampling indication and granularity; receiving a measurement response comprising first measurement results corresponding to the first measurement request; and providing the first measurement results to one or more optimization algorithms for solving the one or more pre-determined optimization problems.

Abstract: Cognitive functions in a network, each comprising a learning module adapted to observe characteristics of the network and an inference module adapted to conclude, from the characteristics observed by the learning module, on a behavior of the network resulting from the observed characteristics, are controlled by halting and/or restarting operation of the learning module of a cognitive function of the cognitive functions, where operation of the inference module of the cognitive function is controlled independently from the halting and/or restarting the operation of the learning module of the cognitive function.