Abstract: The present disclosure relates to on transition to another cell without handover in non-terrestrial network (NTN). In particular, a NTN device transmits information related to a set of NTN devices to a terminal a first device receives assistance information of a NTN device from a second device. The assistance information at least indicated a type of a cell switching. The first device skips buffer flushing and security key updating regardless of the type of cell switching. In this way, it can achieve cell switching without handover.

Abstract: The present disclosure relates to on transition to another cell without handover in non-terrestrial network (NTN). In particular, a NTN device transmits information related to a set of NTN devices to a terminal a first device receives assistance information of a NTN device from a second device. The assistance information at least indicated a type of a cell switching. The first device skips buffer flushing and security key updating regardless of the type of cell switching. In this way, it can achieve cell switching without handover.

Abstract: The present disclosure relates to a reinforcement learning (RL) on beam management. In particular, it utilizes side links capabilities for enabling real data/traffic exchange for RL explorative training step. In this way, it enables a radio system performance friendly RL learning training operation from one side and will utilize the available radio air resources on the other side.

Abstract: A method includes confirming, by a user device with a network node, a set of machine learning (ML) functionality adaptation parameters for the user device to perform adaptation of a ML functionality associated with at least one ML model that is used by the user device to perform a radio access network (RAN)-related function. The set of ML functionality adaptation parameters indicate at least one adaptation cycle during which the user device is to perform the ML functionality adaptation and a validity period for which the set of ML functionality adaptation parameters are valid. The method also includes performing, by the user device, adaptation of the ML functionality during the at least one adaptation cycle.

Abstract: Disclosed is a method comprising providing, to a user equipment, a first configuration that is part of a radio resource control configuration for relaxation measurements, wherein the first configuration comprises legacy hardcoded rules and measurement relaxation parameters for executing a legacy measurement relaxation procedure, receiving a request, from the user equipment, for a second configuration that is for executing a machine learning-based measurement relaxation procedure, providing, to the user equipment, the second configuration, wherein the second configuration comprises one or more of the following: one or more algorithms for deriving relaxation parameters, a length of an evaluation time period, a set of evaluation conditions that are evaluated based on the evaluation time period, reporting periodicity and signal format for reporting a status of the measurement relaxation, receiving, from the user equipment, an indication that the status of the measurement relaxation corresponds to enter.

Abstract: According to an aspect, there is provided an apparatus for performing the following. The apparatus performs cell-specific radio measurements for a plurality of cells or beam-specific radio measurements for a plurality of transmit beams. The apparatus evaluates correlation between results of the cell/beam-specific radio measurements and compares the correlation against one or more pre-defined correlation conditions. In response to the correlation for at least one pair of cells in the plurality of cells or for at least one pair of transmit beams in the plurality of transmit beams satisfying the one or more pre-defined correlation conditions, the apparatus filters the results of the cell-specific or beam-specific radio measurements to remove repetitions of results satisfying the one or more pre-defined correlation conditions and transmits, to another apparatus, a measurement report comprising the filtered results and information on the correlation.

Abstract: There is provided an apparatus comprising means for determining, at a user equipment, a difference between signal strength for a first beam of a serving cell of a network and signal strength for a second beam of the serving cell, means for providing the determined difference as an input for a machine learning model, wherein the output of the machine learning model is numerical data or categorical data, means for determining, based on the output of the machine learning model, that a measurement report should be provided to the network and means for providing the measurement report to the network.

Abstract: Devices, methods and computer programs for reducing radio signal measurements are disclosed. At least some example embodiments may allow minimizing the amount of reported measurements, thereby saving significant amounts of radio resources that can be utilized for data communication, as well as saving energy at user equipment side.

Abstract: Various techniques are provided for a method including receiving, by a centralized unit (CU) from a distributed unit (DU), a measurement report associated with a user equipment, predicting, by the CU, a pilot-route based on the measurement report, configuring, by the CU, a CSIPilot based mobility for the DU based on the pilot-route, and configuring, by the CU, measurement reporting and a CSIPilot based mobility for the user equipment based on the pilot-route.

Abstract: Various techniques are provided for a method including communicating, by a user equipment (UE) to a network device, a message including a measurement relaxation request, receiving, by the UE from the network device, a message including one of a measurement relaxation approval or a measurement relaxation denial, in response to receiving the measurement relaxation approval predicting, by the UE, a measurement relaxation configuration using a machine learning model, communicating, by the UE to the network device, a message including the measurement relaxation configuration, receiving, by the UE from the network device, a message including a measurement relaxation acknowledgement, and reporting, by the UE to the network device, measurements based on the measurement relaxation configuration.

Abstract: Example embodiments may relate to controlling re-training of a machine learning (ML) model deployed at a device. A method may comprise: performing, by a device associated with a communication network, a task with a ML model to obtain an output, wherein the output is configured to be used for performance of a network operation of the communication network; receiving, from an access node of the communication network, feedback data indicative of a cause of a failure of the network operation; and determining, based on the feedback data, to perform at least one of the following: re-training the machine learning model for performing the task, updating at least one parameter of a non-machine learning algorithm associated with performance of the task with the machine learning model, refraining from re-training the machine learning model, or refraining from updating the at least one parameter of the non-machine learning algorithm.

Abstract: Example embodiments of the invention provide at least a method and apparatus to perform communicating by a network node with a user equipment of a communication network a ping-pong offset prediction request message; determining a ping-pong offset prediction wherein the ping-pong offset prediction is taking into account a change in at least one of a speed, trajectory, or received signal levels from a previous serving cell; based on the determining, sending to the network node a ping-pong offset prediction, and wherein based on the ping-pong offset prediction the handover back to the previous serving cell is one of executed or not executed.

Abstract: Some demonstrative embodiments include devices, systems and/or methods of scanning wireless communication channels. For example, a device may include a controller to receive one or more energy indications corresponding to one or more frequency ranges within a wireless communication frequency band, wherein a frequency range of the frequency ranges at least partially covers a plurality of wireless communication frequency channels, and wherein the controller is to scan for at least one wireless communication beacon over one or more channels of the plurality of wireless communication frequency channels, if an energy indication corresponding to the frequency range indicates wireless communication activity over the frequency range.

Abstract: Embodiments of techniques to perform passive and active scanning in a wireless network are presented herein.

Abstract: Embodiments of techniques to perform passive and active scanning in a wireless network are presented herein.

Abstract: A method, device and system for varying the duration of active scanning of a channel in response to noise detected on such channel. If a device detects no noise on such channel, and no probe response is received to the device's probe request, then the duration of scanning on such channel may be limited to a brief dwell time. If noise is detected on such channel then the duration of scanning on such channel may be extended to a longer dwell time.

Abstract: A system and method for measuring latency in voice communications is presented. In an exemplary embodiment, latency is measured by establishing a call between a first and a second telephony device, and measuring a latency between a signal originating at the first telephony device and the signal as it arrives at the second telephony device.

Abstract: A method of synchronizing two voice files is described. A header is added to a test sound file and the augmented file digitized, encoded and transmitted via a packet switched data network. The header comprises a tone which varies by increasing from a very low amplitude to a precisely detectable peak value, and then decreasing to a very low amplitude again. The voice data in the file follows the peak amplitude by a known delay. The header peak value is precisely located in time by searching for the peak amplitude. Adding the known delay precisely locates, temporally, the beginning of the voice data. This method allows the synchronization of the original file with its transmitted version to a very high precision.

Abstract: A system and method for measuring latency in voice communications is presented. In an exemplary embodiment, latency is measured by establishing a call between a first and a second telephony device, and measuring a latency between a signal originating at the first telephony device and the signal as it arrives at the second telephony device.