Abstract: According to an example aspect of the present invention, there is provided a method comprising receiving or transmitting at least one reference signal, wherein the at least one reference signal is transmitted according to a reference signal configuration, and wherein the reference signal configuration indicates a power-muting for at least one inner resource block of a plurality of resource blocks assigned in the frequency-domain to the at least one reference signal, and a power-boosting for at least two edge resource blocks of the plurality of resource blocks.

Abstract: Example embodiments of the present disclosure are related to artificial intelligence/machine learning (AI/ML) model test. A first apparatus transmits test configuration information to a second apparatus, the test configuration information indicating a test mode of an AI/ML model with respect to at least one transmission and reception unit (TRP), the at least one TRP being arranged within an environment based on a test plan for at least one test channel indicator. The first apparatus receives, from the second apparatus, at least one predicted channel indicator for the at least one TRP, the at least one predicted channel indicator being derived by the second apparatus using the AI/ML model. The first apparatus determines a test result for the AI/ML model based on a comparison between the at least one predicted channel indicator and the at least one test channel indicator, the test result indicating whether the AI/ML model is validated.

Abstract: A network element evaluates performance of a first AI/ML model being used by a UE. The network element sends, based on the evaluation, configuration to a network entity involved in performing model retuning of the first AI/ML model to aid in the model retuning. The network element monitors and evaluates performance of a second AI/ML model that is a retuned version of the first AI/ML model. The first and second AI/ML models are from a same lineage of AI/ML models. The network element stores, in response to the evaluation of the second AI/ML model, the second AI/ML model for use by other UE(s). A UE receives the configuration, and performs operation(s) to aid in the performing retuning. The retuning creates a second AI/ML model that is a retuned version of the first AI/ML model. The UE switches from the first AI/ML model to the second AI/ML model.

Abstract: Example embodiments of the present disclosure relate to methods, devices, apparatuses and computer readable storage medium for a model monitoring for positioning, especially for assisted Artificial Intelligence/Machine Learning (AI/ML) positioning without measured ground truth (GT). The method comprises: determining, based on three or more transmission reception points, TRPs, at least one reference location associated with a positioning performance monitoring of a second apparatus; generating assistance data for monitoring a positioning performance at the second apparatus at least comprising at least one of: respective positioning measurement data of at least one positioning measurement type in the at least one reference location; respective monitoring metric associated with the at least one positioning measurement type in the at least one reference location; or the at least one reference location; and transmitting the assistance data to a second apparatus.

Abstract: Method, apparatuses, and computer program products provide means for establishing a mechanism through which a network can configure user equipment to determine how to process an indication of region of the user equipment location when establishing a network connection. An example method includes: receiving, from a visited network, a first indication of a region of an apparatus location, where the apparatus includes a User Equipment (UE); and determining, based on a second indication in the apparatus, whether the apparatus shall ignore the first indication for network selection, where the second indication is received from a home network of the apparatus via a container transparent to the visited network. The region of the apparatus location may include a country of the apparatus location. The container may include a Steering of Roaming transparent container. The container may be delivered via a rejection message.

Abstract: Systems, methods, apparatuses, and computer program products for verifying positioning accuracy. One method may include transmitting, by testing equipment, a request to measure location coordinates to a device, receiving, by the testing equipment, from the device, a model inference comprising at least one location coordinate, and determining, by the testing equipment, whether a difference between the model inference and at least one ground truth is within a threshold value for a defined number of samples, wherein the at least one ground truth comprises at least one known location coordinate of a test point.

Abstract: Example embodiments of the present disclosure relate to methods, devices, apparatuses and computer readable storage medium of noisy positioning data processing. In a method, a first apparatus obtains measurement data and first positioning data associated with the measurement data. The first apparatus generates second positioning data from the first positioning data based on network assistance information. The first apparatus transmits at least the measurement data and the second positioning data. In this way, an accuracy of the positioning data can be improved.

Abstract: According to an example aspect of the present invention, there is provided an apparatus configured to transmit, to a network, a first sounding reference signal, obtain, from the first sounding reference signal and based on a peak-to-average power ratio, PAPR, adjustment signal received in the apparatus from the network, a second sounding reference signal with lower PAPR than the first sounding reference signal, and transmit, to the network, the second sounding reference signal.

Abstract: A method performing by an apparatus, including: receiving (501), from a network entity or transmitting to a user device, one or more feature set lists each including a plurality of feature sets possibly in a priority order or rank order associated with the apparatus or the user device, wherein the one or more feature set lists are based on at least one of the following: one or more bandwidth parts configured for the apparatus or the user device, one or more bandwidth capabilities of the apparatus or the user device, or one or more radio environment scenarios; and classifying or selecting channel measurements of the one or more received signals per measurement by the apparatus or by the user device, to estimate a likelihood of a propagation condition classification including one of: line-of-sight (LOS), non-line-of-sight (NLOS) and obstructed line-of-sight, based on the feature sets to determine positioning of the apparatus or the user device.

Abstract: Embodiments of the present disclosure disclose devices, methods and apparatuses for transforming sampled signal. The terminal device receives, from a network device, a request that the terminal device applies a transformation operation to a sampled signal; obtains a transformed signal by applying the transformation operation to the sampled signal; and transmits the transformed signal to the network device. By the proposed processing for the sampled signal, the terminal device-specific RF and/or baseband (BB) architecture becomes transparent to the network device.

Abstract: A method comprising: determining at a first device a requirement for a spatial distribution associated with training samples for a machine learning model, wherein the machine learning model is used in solving a positioning task; performing at least one of: transmitting, to the second device, information indicating the requirement for training the machine learning model, or training the machine learning model by using a training dataset comprising training samples satisfying the requirement.

Abstract: A method performed by a network node (110) in a wireless communications network (100) for enabling a timing reference in a second wireless device (122) for use in Sidelink, SL, transmissions over a SL communication link (133) with a first wireless device (121) is provided. The network node estimates (201) a propagation delay of SL transmissions over the SL communication link (133) between the first and second wireless device (122). Then, the network node (110) transmits, to the second wireless device (122), timing reference information based on a timing reference originating from a time reference node in a wireless communications network (100), and on the estimated propagation delay of SL transmissions over the SL communication link (133) between the first and second wireless device (122). A network node (110) for enabling a timing reference in a second wireless device (122) for use in SL transmissions over a SL communication link (133) with a first wireless device (121) is also provided.

Abstract: Strontium oxide (SrO) nanoparticle and various concentrations of chitosan (CS)-doped SrO nanocomposite were synthesized via co-precipitation method. A variety of characterization techniques including were done for characterizing and qualifying the nanocomposite. X-ray powder diffraction affirmed cubic and tetragonal structure of SrO nanoparticle and CS-doped SrO nanocomposite with a decrease in crystallinity upon doping. Fourier transform infrared spectrum endorsed existing functional groups on CS/SrO surfaces while d-spacing was estimated using high resolution Transmission electron microscopes images. UV-Visible and Photoluminescence spectroscopy spectra showed an increase in band gap energies with an increase in doping concentration. Elemental composition of CS-doped SrO nanocomposite deposited with different doping concentrations was studied using Energy dispersive Spectroscopy.

Abstract: Strontium oxide (SrO) nanoparticle and various concentrations of chitosan (CS)-doped SrO nanocomposite were synthesized via co-precipitation method. A variety of characterization techniques including were done for characterizing and qualifying the nanocomposite. X ray powder diffraction affirmed cubic and tetragonal structure of SrO nanoparticle and CS-doped SrO nanocomposite with a decrease in crystallinity upon doping. Fourier transform infrared spectrum endorsed existing functional groups on CS/SrO surfaces while d-spacing was estimated using high resolution Transmission electron microscopes images. UV-Visible and Photoluminescence spectroscopy spectra showed an increase in band gap energies with an increase in doping concentration. Elemental composition of CS-doped SrO nanocomposite deposited with different doping concentrations was studied using Energy dispersive Spectroscopy.

Abstract: A method may include receiving, by a first user device from a network node or a second user device, 1) a positioning measurement report including at least one positioning measurement measured by a reference device, and 2) reference positioning-related information to be used for testing and/or validating a machine learning model; determining estimated positioning-related information as outputs of the machine learning model based on at least a portion of the positioning measurement report as inputs to the machine learning model; determining a performance indication of the machine learning model based on the reference positioning-related information and the estimated positioning-related information, wherein the performance indication indicates a performance or accuracy of the machine learning model; and performing, by the first user device, an action based on the performance indication.

Abstract: Systems, methods, apparatuses, and computer program products for Rx-Tx RTT estimation are provided. One method may include transmitting, by a network element, the reference signal to another network element and recording a time stamp (t0) of a time of transmission of the reference signal, and receiving an estimated Rx-Tx measurement from said another network element and recording a time stamp (t3) of a time of receipt of the estimated Rx-Tx measurement. The method may also include calculating a Rx-Tx measurement at the network element based on a difference between the time stamp (t0) of the time of transmission of the reference signal and the time stamp (t3) of the receipt of the estimated Rx-Tx measurement, and determining a propagation delay based on a difference between the received estimated Rx-Tx measurement and the calculated Rx-Tx measurement.

Abstract: Disclosed is a method comprising determining one or more radio access nodes based on available location information of a target user device and at least one neighboring user device of the target user device; transmitting a sidelink information request to the one or more radio access nodes, wherein the sidelink information request indicates requesting sidelink information of the target user device and the at least one neighboring user device of the target user device; receiving the sidelink information from the one or more radio access nodes; and transmitting, to at least one of the one or more radio access nodes and/or the at least one neighboring user device, at least a part of a locality database and/or a sidelink positioning configuration for positioning the target user device, wherein the locality database and/or the sidelink positioning configuration are based at least on the sidelink information.

Abstract: CS-doped SrO nanocomposite were successfully synthesized through co-precipitation route for bactericidal activities. Effect of CS doping on morphological features, optical properties, elemental composition and phase constitution on CS-doped SrO nanocomposite was analyzed. XRD analysis confirmed tetragonal and cubic structures of SrO nanoparticles and CS-doped SrO nanocomposite. UV-vis spectroscopy was used to obtain 4.19 eV of SrO nanoparticles while emission spectra of doped SrO showed blueshift upon CS doping with multi-concentration. Interlayer d-spacing attained from HRTEM micrographs well matched with XRD d-spacing. Purity content of prepared nanostructures was measured with EDS analysis. Overall, 0.06:1 showed significant antibacterial activity against both Gram +ve and -ve bacterial isolates. Thus, CS-doped SrO nanocomposite can be used in modem medicine as an alternative antibacterial to overcome the development of resistance to antibiotics.

Abstract: Strontium oxide (SrO) nanoparticle and various concentrations of chitosan (CS)-doped SrO nanocomposite were synthesized via co-precipitation method. A variety of characterization techniques including were done for characterizing and qualifying the nanocomposite. X ray powder diffraction affirmed cubic and tetragonal structure of SrO nanoparticle and CS-doped SrO nanocomposite with a decrease in crystallinity upon doping. Fourier transform infrared spectrum endorsed existing functional groups on CS/SrO surfaces while d-spacing was estimated using high resolution Transmission electron rnicroscopes images. UV-Visible and PL Photoluminescence spectroscopy spectra showed an increase in band gap energies with an increase in doping concentration. Elemental composition of CS-doped SrO nanocomposite deposited with different doping concentrations was studied using Energy dispersive Spectroscopy.

Abstract: Embodiments described herein provide methods and apparatus for transmitting at least one data stream comprising a plurality of samples between a first wireless device and a second wireless device using machine-to-machine communication. A method in the first wireless device comprises receiving the at least one data stream; generating a first data packet comprising N of the plurality of samples, wherein N is an integer value of greater than or equal to 2; and transmitting the first data packet to the second wireless device.