Abstract: A first network analysis node in a network receives, from a device, a first message comprising information indicative of a subscription, for the device, to network analytics information, and sends, to a second network node, a second message comprising information indicative of a request for a transfer of at least part of the subscription to network analytics information to a second network analysis node.

Abstract: An apparatus, system or method for processing a sequence of data can involve determining an availability of a computational resource; adapting, based on the availability, a neural network to set a limit on use of the computational resource by the neural network during processing of a sequence of data, wherein the one or more processors being configured to adapt the neural network to set the limit comprises the one or more processors being configured to modify an update control included in the neural network based on a windowing function; and processing at least a portion of the sequence of data by the adapted neural network using the computational resource in accordance with the limit.

Abstract: In one implementation, the AI/ML model is first split into several unitary chunks that correspond to sub-parts of the model. Then an aggregation of unitary chunks is made by considering the download time, inference time of unitary chunks, and/or device constraints. The first split corresponds to a first chunk of AI/ML layers that, once downloaded, is useable as is, and generates intermediate results based on some sensing/perception data. As soon as a new chunk arrives, it is used to generate new results based on the intermediate data of the previous chunk. Since download and inference are parallelized, a final result can be generated earlier than with the full sequential method. In addition, as soon as the inference ends on a chunk, this chunk may be removed from the device. Several AI/ML model split methods are provided to generate model subsets/chunks for different model architectures.

Abstract: The present disclosure relates to a method including obtaining metadata upon training a first Deep Neural Network and embedding the obtained metadata in a signal. The present disclosure relates to a method including obtaining metadata related to a prior training of a first Deep Neural Network and adapting a model of a second Deep Neural Network using the obtained metadata. The present disclosure also relates to the corresponding devices, computer storage medium and signal.

Abstract: In general, at least one example of an embodiment can involve determining a constraint associated with processing a sequence of data, adapting a neural network based on the constraint, wherein adapting the neural network comprises modifying, based on the constraint, a characteristic of a decision function included in the neural network; and enabling processing of at least a first portion of the sequence of data utilizing the adapted neural network and in accordance with the constraint.

Abstract: In general, at least one example of an embodiment can involve selecting a neural network from a plurality of neural networks based on an indication of resource availability and processing data using the selected neural network in accordance with the resource availability.

Abstract: Methods, apparatuses, systems, etc., directed to quality of experience (QoE) data analytics for multiple wireless transmit and receive units (WTRUs) in 5G networks are disclosed herein. In an embodiment, a method may comprise receiving a request message requesting a quality of service (QoS) configuration related to at least one WTRU involved in an application for obtaining a target QoE of the application. The first method may further comprise transmitting the QoS configuration obtained based on the target QoE. In an embodiment, a method may comprise transmitting a first request signal for subscribing to a service data from an application and receiving a first response signal comprising at least one identifier of at least one WTRU involved in the application, the first response signal may comprise at least one indication of at least one individual contribution of the at least one WTRU to a QoE of the application.

Abstract: A method for selectively changing an activation status of an access point in a wireless system is described. The wireless system includes a plurality of wireless access points having at least one device for extending wireless signal coverage coupled thereto and a controller. The controller is configured to change the activation status of the at least one extender coupled to the one or more of the plurality of wireless access points based on connection quality of a user device to the wireless system.

Abstract: A method for selectively changing an activation status of an access point in a wireless system is described. The wireless system includes a plurality of wireless access points having at least one device for extending wireless signal coverage coupled thereto and a controller. The controller is configured to change the activation status of the at least one extender coupled to the one or more of the plurality of wireless access points based on connection quality of a user device to the wireless system.