Abstract: It is provided a method, including providing location information indicating a location of a terminal to a radio-independent localization and tracking system; evaluating at least one of environmental information and tracking information received from the radio-independent localization and tracking system with respect to the terminal in response to providing the location information; managing a resource for serving the terminal based on the at least one of the environmental information and the tracking information, wherein the environmental information includes information about an environment of the terminal, and the tracking information includes information about a track of the terminal.

Abstract: An example method, apparatus, and computer-readable storage medium are provided for exploration procedures for network optimization. In one example implementation, the method may include generating, by a first network element, exploration data, the exploration data being generated by the first network element for evaluating performance at a second network element; transmitting, by the first network element, the exploration data to the second network element; and receiving, by the first network element, exploration data feedback from the second network element, the exploration data feedback received from the second network element based on processing of the exploration data by the second network element.

Abstract: An example method, apparatus, and computer-readable storage medium are provided for early data transmission (EDT) random access (RA) procedure for dual connectivity or carrier aggregation. In an example implementation, the method may include receiving configuration information from a network node, initiating an early data transmission (EDT) random access (RA) procedure with one or more cells indicated in the configuration information, determining a secondary cell of the one or more cells as a preferred secondary cell, and transmitting data to the preferred secondary cell.

Abstract: Modified type II control state information (CSI) reporting for achieving explicit CSI knowledge is provided. A method may be performed by providing, to a user equipment, a plurality of channel state information reference signals over a single beam and receiving channel state information from the user equipment. Explicit channel state information may be determined from a channel transfer function for some or all frequency subbands based on the received channel state information.

Abstract: An improved NR-U framework allows a gNB to repeat RLM-RS with the same index over each sub-band of an unlicensed spectrum. For example, a RLM-RS with the same indices are repeated by the gNB over the sub-bands of a wideband channel in an unlicensed spectrum. By monitoring the different sub-bands, a UE may find one or more sub-bands in which the RLM-RS having a particular index is successfully transmitted at a subsequent time.

Abstract: Devices, methods and computer programs for enhancing a data map of objects via object specific radio frequency parameters are disclosed. Channel state information measurements for a surrounding of a network node and a corresponding radio channel simulation are accessed. A set of radio frequency parameters for each interaction point is estimated.

Abstract: Apparatuses and methods in a communication system are disclosed. A channel predictor is determined to be used in a connection between a base station and a terminal device. The predictor is trained using channel state information of the connection and a predictor function determined to be used both in the base station and terminal device. The base station calculates a predicted state of the channel utilising the predictor function and transmits payload data to the terminal device. The base station receives update information to the predicted state of the channel from the terminal device, where the update information is the variation between the channel predicted at the terminal device and the measured channel.

Abstract: An improved NR-U framework allows a gNB to repeat RLM-RS with the same index over each sub-band of an unlicensed spectrum. For example, a RLM-RS with the same indices are repeated by the gNB over the sub-bands of a wideband channel in an unlicensed spectrum. By monitoring the different sub-bands, a UE may find one or more sub-bands in which the RLM-RS having a particular index is successfully transmitted at a subsequent time.

Abstract: Embodiments of the present disclosure disclose devices, methods and apparatuses for channel prediction. A terminal device obtains model configuration information. The model configuration information indicates a first prediction model for channel prediction. The terminal device receives overfitting configuration information from a network device. The overfitting configuration information indicates configuration on reference signals for overfitting by the first prediction model. The terminal device reports channel information predicted based on the first prediction model and the overfitting configuration information.

Abstract: Various example embodiments relate to reception of chirp signals. A receiver may receive a signal including at least one chirp spread spectrum waveform. The receiver determines whether the signal includes overlapping chirp waveforms. A decoding method may be selected accordingly. Apparatuses, methods, and computer programs are disclosed.

Abstract: Systems, methods, apparatuses, and computer program products for testing user equipment (UE) machine learning-assisted radio resource management (RRM) functionalities are provided. One method may include selecting a radio resource management (RRM) functionality to be tested for a user equipment (UE) having advertised machine learning (ML)-assistance capabilities, initializing a machine learning (ML)-assistance model in the user equipment based on the advertised machine learning (ML)-assistance capabilities, generating one or more input test signals and corresponding reference output test conditions depending on the machine learning (ML)-assistance radio resource management (RRM) functionality under test, and activating UE machine learning (ML)-assistance functionality and provisioning, to the user equipment, a test sequence with the generated input test signals and corresponding reference output conditions.

Abstract: A method for performing multiple-input and multiple-output channel estimation includes: generating, using a first machine-learning model, an initial set of estimated channel information from a first input set of channel information, wherein the first input set of channel information corresponds to a first plurality of radio-frequency chains, and wherein the estimated set of channel information corresponds to the first plurality of radio-frequency chains and a second plurality of radio-frequency chains; generating, using a second machine-learning model, a set of estimated channel phases from the initial set of estimated channel information and a second input set of channel information, wherein the second set of input channel information corresponds to the second plurality of radio-frequency chains; and combining the initial set of estimated channel information and the set of estimated channel phases to generate an enhanced set of estimated channel information.

Abstract: Inter-alia, an apparatus is disclosed comprising: at least one power amplifier coupled to at least one radio frequency power storage device, wherein the at least one power amplifier is configured to supply power to the at least one radio frequency power storage device, wherein the at least one power amplifier provides power to be used to amplify one or more radio frequency signals; wherein the at least one radio frequency power storage device is configured to store the power of the at least one power amplifier for a certain time period; and one or more antenna elements coupled to the at least one radio frequency power storage device, wherein the at least one radio frequency power storage device is configured to output stored power to at least one of the one or more antenna elements, wherein the power is output variably dependent upon a power demand of a required radio frequency power and/or amplitude needed to transmit the one or more radio frequency signals, wherein the power demand represents a power demand

Abstract: Inter-alia, an apparatus is disclosed comprising: at least one power amplifier coupled to at least one radio frequency power storage device, wherein the at least one power amplifier is configured to supply power to the at least one radio frequency power storage device, wherein the at least one power amplifier provides power to be used to amplify one or more radio frequency signals; wherein the at least one radio frequency power storage device is configured to store the power of the at least one power amplifier for a certain time period; and one or more antenna elements coupled to the at least one radio frequency power storage device, wherein the at least one radio frequency power storage device is configured to output stored power to at least one of the one or more antenna elements, wherein the power is output variably dependent upon a power demand of a required radio frequency power and/or amplitude needed to transmit the one or more radio frequency signals, wherein the power demand represents a power demand

Abstract: Method, comprising receiving a terminal location information or a location-like information from a terminal; selecting one or more first pairs of prior channel information among one or more stored first pairs of prior channel information based on the terminal location information or the location-like information, respectively; inputting the terminal location information or the location-like information, respectively, and the selected one or more first pairs of prior channel information into a trained interpolation neural network to obtain a first estimation of a channel between the terminal and a base station as an output from the interpolation neural network; providing the weights of the trained neural network to the terminal; wherein each of the one or more first pairs of prior channel information comprises a location information related to a respective prior channel and a first representation of the respective prior channel.

Abstract: Method, comprising receiving a terminal location information or a location-like information from a terminal; selecting one or more first pairs of prior channel information among one or more stored first pairs of prior channel information based on the terminal location information or the location-like information, respectively; inputting the terminal location information or the location-like information, respectively, and the selected one or more first pairs of prior channel information into a trained interpolation neural network to obtain a first estimation of a channel between the terminal and a base station as an output from the interpolation neural network; providing the weights of the trained neural network to the terminal; wherein each of the one or more first pairs of prior channel information comprises a location information related to a respective prior channel and a first representation of the respective prior channel.

Abstract: A method may include transmitting, by a network node within a wireless network to a reference node, a first precoded tracking signal based on estimated precoding weights that are estimated to provide a predetermined signal at the reference node; receiving, by the network node from the reference node, a message including information related to whether or not the predetermined signal was received at the reference node based, at least in part, on the first precoded tracking signal transmitted by the network node; performing the following, by the network node, if the predetermined signal was not received at the reference node: adjusting one or more transmission parameters of the network node, that is estimated to more accurately provide the predetermined signal at the reference node; and transmitting, by the network node, a second precoded tracking signal based on adjusted transmission parameters.

Abstract: According to an example embodiment, an example method may include estimating, by a receiver device in a wireless network, a set of one or more active beams for the receiver device based on an estimated receive power of each active beam to obtain an estimated set of one or more active beams for the receiver device; receiving, by the receiver device, verification information associated with the estimated set of one or more active beams for the receiver device; and, verifying, by the receiver device based on the verification information, the estimated set of one or more active beams for the receiver device.

Abstract: An example method, apparatus, and computer-readable storage medium are provided for exploration procedures for network optimization. In one example implementation, the method may include generating, by a first network element, exploration data, the exploration data being generated by the first network element for evaluating performance at a second network element; transmitting, by the first network element, the exploration data to the second network element; and receiving, by the first network element, exploration data feedback from the second network element, the exploration data feedback received from the second network element based on processing of the exploration data by the second network element.

Abstract: According to an aspect, there is disclosed a method comprising receiving, from a producer node, producer node capabilities comprising a machine learning based assistance capability, the machine learning based assistance capability comprising machine learning based functionalities, each machine learning based functionality comprising a machine learning entity and at least one machine learning mode associated with the machine learning entity. According to another aspect, there is disclosed a method comprising identifying producer node capabilities comprising a machine learning based assistance capability, the machine learning based assistance capability comprising machine learning based functionalities, each machine learning based functionality comprising a machine learning entity and at least one machine learning mode associated with the machine learning entity; and causing transmission of the producer node capabilities to a consumer node.