Abstract: According to some embodiments, a method performed by a wireless device comprises obtaining a priority associated with each of one or more fields of an uplink transmission. An interpretation of the one or more fields is based on a machine learning model and is undefined with respect to an existing uplink control information (UCI) type. The method further comprises allocating physical uplink control channel (PUCCH) resources for one of the one or more fields based on the obtained priority and transmitting the uplink transmission using the allocated PUCCH resources.

Abstract: According to some embodiments, a method performed by a wireless device comprises obtaining a priority associated with each of one or more fields of an uplink transmission. An interpretation of the one or more fields is based on a machine learning model and is undefined with respect to an existing uplink control information (UCI) type. The method further comprises applying a resource element mapping rule to one of the one or more fields based on the obtained priority and transmitting the uplink transmission based on the applied resource element mapping.

Abstract: A method (700) by a user equipment, UE (112), for reporting Channel State Information, CSI, includes transmitting, to a network node (110), an Artificial Intelligence-based, AI-based, CSI report. The AI-based CSI report includes a plurality of parts. Each of the plurality of parts are transmitted on a respective one of a plurality of uplink control information, UCI, parts. An interpretation of at least one bit of at least one of the plurality of parts is based on an output of a machine learning model.

Abstract: According to some embodiments, a method performed by a wireless device comprises obtaining a priority associated with each of one or more fields of an uplink transmission. An interpretation of the one or more fields is based on a machine learning model and is undefined with respect to an existing uplink control information (UCI) type. The method further comprises allocating a transmit power to one of the one or more fields based on the obtained priority and transmitting the uplink transmission based on the allocated transmit power.

Abstract: According to some embodiments, a method performed by a wireless device comprises obtaining a priority associated with each of one or more fields of an uplink transmission. An interpretation of the one or more fields is based on a machine learning model and is undefined with respect to an existing uplink control information (UCI) type. The method further comprises transmitting the uplink transmission, wherein one of the one or more fields is included in the uplink transmission based on the obtained priority.

Abstract: A method, system and apparatus are disclosed. A wireless device is provided. Wireless device is configured to perform measurements on at least one first reference signal resource and at least one second reference signal of a plurality of reference signal resources indicated by a reference signal configuration, the measurements being performed based on a measurement report configuration associated with the plurality of reference signal resources. The wireless device is configured to store the measurements for at least one of training and monitoring a machine learning, ML, model configured to predict at least one of at least one best beam and at least one K-best beam associated with at least one downlink, DL, reference signal transmitted by the network node.

Abstract: There are provided mechanisms for monitoring search spaces. A first method performed by a wireless device comprises receiving an Orthogonal Frequency-Division Multiplexing (OFDM) symbol in a downlink slot. At least part of the OFDM symbol is included in a device-specific search space and in a common search space. The first method comprises monitoring the device-specific search space for at least one device-specific reference signal (RS) and monitoring the common search space for at least one non-device-specific RS. In a second method, a radio access network node transmits an OFDM symbol included in a device-specific search space and in a common search space. The device-specific search space contains a device-specific RS, or the non-device specific search space contains a non-device-specific RS, or both of these apply.

Abstract: Methods and systems are described for indicating and configuring machine learning (ML) model support in a user equipment (UE) in a network. A UE can provide model support information to a node, including a ML type and/or version information of at least one model associated with a certain functionality. A network node uses the support information, and optionally previous model ID allocation information in current or other nodes, to determine at least one model ID, and signals it to the UE. The model ID refers to the model in subsequent model handling-related signaling between the UE and the node. The node assigns the model ID such that the UE and the node are aware that a given model ID refers to a given model or model version (e.g., the mapping is unique for the UE). The model ID can also be unique within a functional area, for example CSI reporting.

Abstract: Radio communication systems, devices and methods for enabling variable subband configuration of one or more search spaces. In a first time interval, a UE attempts to decode one or more control channels on downlink or sidelink resources of a first sub band configuration. In a second time interval, the UE attempts to decode one or more control channels on downlink or sidelink resources of a second sub band configuration. The first subband configuration spans a first frequency range and the second subband configuration spans a second frequency range different from the first frequency range.

Abstract: According to some embodiments, a method is performed by a wireless device for fallback operation of a machine learning (ML) model. The method comprises: transmitting a message indicating a capability of the wireless device for supporting a combination of at least one ML-based feature for a functionality and at least one fallback feature for the functionality to a network node; operating the at least one ML-based feature for the functionality; and operating the at least one fallback feature for the functionality.

Abstract: Limited uplink bandwidth radio network devices are supported on wide bandwidth uplink carriers by configuring and controlling the use of multiple control regions within the uplink carrier bandwidth. In some embodiments, the total bandwidth of the uplink carrier is divided into a plurality of sub-band portions, wherein each sub-band portion includes at least one control region nominally dedicated to the transmission of uplink control signaling. Radio network devices are configured with a specified portion of the uplink carrier bandwidth for use for uplink transmission, which includes at least one control region. Radio network devices may be provisioned or provided with information regarding control regions reserved for control signaling by other radio network devices. The radio network devices may be dynamically configured to allow or suppress uplink data signaling in specified control regions, or portions thereof.

Abstract: There are provided mechanisms for monitoring search spaces. A first method performed by a wireless device comprises receiving an Orthogonal Frequency-Division Multiplexing (OFDM) symbol in a downlink slot. At least part of the OFDM symbol is included in a device-specific search space and in a common search space. The first method comprises monitoring the device-specific search space for at least one device-specific reference signal (RS) and monitoring the common search space for at least one non-device-specific RS. In a second method, a radio access network node transmits an OFDM symbol included in a device-specific search space and in a common search space. The device-specific search space contains a device-specific RS, or the non-device specific search space contains a non-device-specific RS, or both of these apply.

Abstract: A method performed by a user equipment (UE) is provided. The method comprises sending, in response to a request from a network node, information associated with one or more machine-learning (ML) models operable by the UE; and receiving, from a network node, a representation of at least one modification of one or more variables associated with at least one ML model of the one or more ML models. The one or more variables are based on the information associated with the one or more ML models, and the at least one modification of the one or more variables facilitates at least partially correcting or preventing performance degradations of the at least one ML model.

Abstract: A method of operating a user equipment, UE, that is served by a radio access network, RAN, includes determining an uplink duty cycle for uplink transmission by the UE to the RAN, and selecting a peak uplink power level in response to the determined uplink duty cycle. A method of operating a UE according to some embodiments that communicates with a RAN via a plurality of component carriers includes determining an aggregate uplink duty cycle based on scheduled uplink time intervals across the plurality of component carriers, and selecting a peak uplink transmission power level for transmitting uplink signals on the plurality of component carriers in response to the determined aggregate uplink duty cycle. Related devices are also disclosed.

Abstract: Disclosed are methods, apparatuses, and systems for using Artificial Intelligence (AI) and Machine Learning (ML) in cellular networks. In one aspect, a method is performed by a first node. The method includes sending, to a second node, a first message that indicates a request to update or reconfigure a functionality in the first node related to an ML-model or another functionality in which the ML-model is a part. The method further includes receiving, from the second node, a second message responsive to the first message, and performing an update of the functionality related to the ML-model based on the second message.

Abstract: Methods and systems are described for a user equipment (UE) to monitor, detect problems or failures, and perform resolution actions in a machine learning (ML) model. Configurations or parameters for the monitoring, detecting, and/or resolving can be provided by a network node. The UE can perform one or more resolution actions that can be said to be autonomous actions, as they are triggered by the detection of the ML model problem, not in response to a network command or message received after the ML model problem is detected.

Abstract: The present disclosure describes a method performed by a user equipment (UE) for reporting the performance of at least one machine-learning (ML) model to a cellular telecommunications network. Some exemplary embodiments include the UE utilizing at least one ML model, generating one or more reports or reportable information of a performance of the at least one ML model, and reporting the one or more reports or reportable information to a network. Associated devices and systems are also provided herein.

Abstract: A method performed by a user equipment (UE) for detecting performance degradation for machine learning (ML)-model performance of the UE is provided. The method comprises sending, to a network node, at least one ML-model output; and sending, to the network node, communication information, wherein the communication information is associated with communication performance of the UE. The at least one ML-model output and the communication information associated with communication performance of the UE facilitate determination of a cause of degraded performance of the UE including at least one of a cause related to the ML model or a cause unrelated to the ML model.

Abstract: A method (1100) by a radio node (110, 210, 310) includes transmitting (1102), to another radio node (120, 220, 320), information indicating an activation or a deactivation of one or more AI and/or ML models at the radio node. For example, the radio node may include a UE and the other radio node may include a base station such that the base station is able to inform and/or suggest modifications in the node configurations to enhance communication performance and model selection at the UE.

Abstract: A wireless device (120) and a method for enabling access to a radio network node (110) as well as a radio network node (110) and a method for enabling the wireless device (120) to enable access to the radio network node (110) are disclosed. The wireless device (120) supports a first transmission bandwidth. The radio network node (110) operates a carrier on a second transmission bandwidth. The first transmission bandwidth is narrower than the second transmission band width. The wireless device (120) obtains (202) information about the carrier. The wireless device (120) determines (206) a mapping scheme for mapping a set of enumerable elements, e.g. resource blocks or sequence elements, to frequency ranges based on the information about the carrier.