Abstract: A base station (BS) includes a transceiver configured to receive channel state information (CSI) from a user equipment (UE). The BS also includes a processor operably coupled to the transceiver. The processor is configured to obtain one or more input tensors in a frequency domain, the one or more input tensors associated with the CSI received from the UE. The processor is also configured to transition time interval (TTI)-wise normalize the one or more input tensors, delay-angle domain transform a result of the TTI-wise normalization, and generate a next time step CSI prediction for the UE based on a result of the delay-angle domain transformation.

Abstract: A method of channel estimation includes: receiving, at a first electronic device, a signal from a second electronic device on a channel, the received signal modified by a noise, the channel associated with a channel matrix; obtaining a noisy channel based on the received signal and a least squares estimate of the channel matrix; preprocessing the noisy channel to remove at least one of a timing offset, a multiuser interference, or an intercell interference; inputting the preprocessed noisy channel to a CE model trained to perform CE based on the preprocessed noisy channel, the CE model comprising residual learning networks; and estimating the channel matrix based on denoising of the preprocessed noisy channel.

Abstract: A method for channel estimation includes: receiving, by a first electronic device, a signal indicative of a state of a channel from a second electronic device, the signal being associated with a channel matrix and corrupted by a noise; obtaining a noisy image of the channel in a first domain, the noisy image being a least squares estimate of the channel matrix; transforming the noisy image into a second domain; and performing channel estimation (CE) of the channel based on (i) the transformed noisy image, (ii) a CE model configured to denoise an input signal, and (iii) a sparsity of channel state information (CSI) in the transformed noisy image in the second domain.

Abstract: Methods and apparatuses for automating configuration management in cellular networks. A method of a UE comprises: training, based on historical samples, a regression model y using samples obtained from a set of parameters, wherein the regression model y comprises a function of a first term X and a second term h; and predicting, based on the regression model y, a target KPI to capture parameter impacts corresponding to the second term h.

Abstract: A method includes acquiring, by a processor of an electronic device, information associated with channel and noise covariances. The method includes determining one or more time-domain rectangular filters based on the information associated with the channel and noise covariances. The method includes generating one or more convolutional kernels based on the one or more rectangular filters applied to the channel and noise covariances in a time-domain. The method includes generating a codebook based on the one or more convolutional kernels, the codebook comprising N codewords. Further, the method can include establishing a communication link to a gNB configured to: receive a reference signal from a user equipment; receive the codebook; calculate channel statistics using a low complexity algorithm; execute a decision tree classifier to select a codeword from the codebook stored in memory of the gNB; and apply the selected codeword as convolution kernel for channel estimation.

Abstract: A method includes partitioning a set of configuration management (CM) data for one or more cellular network devices into multiple distinct time intervals, each time interval associated with a distinct set of CM settings at the one or more cellular network devices, the CM data comprising multiple CM parameters. The method also includes determining a regression model based on the set of CM data. The method also includes applying the regression model to compute a distinct set of scores and compare the set of scores to estimate whether a performance of the one or more cellular network devices has changed during a second time interval relative to a first time interval.

Abstract: An electronic device includes a transceiver configured to receive information related to an action request or rule. The electronic device further includes a processor operatively coupled to the transceiver. The processor is configured to determine that the action is location based, and identify a zone within which the electronic device is located. The identification of the zone is based on at least one of a first reception by the transceiver of at least one signal indicative of a location of the electronic device, and a second reception by the transceiver of at least one response to a transmission of a signal indicative of a location of the electronic device. The processor is further configured to trigger an action based on the action request or rule and the identified zone.

Abstract: A method includes selecting a subset of access points from among multiple candidate access points associated with a building. The method also includes receiving Wi-Fi signal strength data from the subset of access points. The method further includes using a machine learning (ML) localization algorithm to determine a proximity of a device within the building based on the received Wi-Fi signal strength data.

Abstract: A method includes partitioning a set of configuration management (CM) data for one or more cellular network devices into multiple distinct time intervals. The method also includes determining one or more temporal points of interest in each time interval based on whether CM changes exist during that time interval. The method also includes, for each temporal point of interest in each time interval, identifying a first set of data samples before that temporal point of interest and a second set of data samples after that temporal point of interest, and averaging features and a target key performance indicator (KPI) in the first set of data samples and in the second set of data samples. The method also includes performing regression analysis to determine an impact of the features on the target KPI.

Abstract: Methods and apparatuses for automating configuration management in cellular networks. A method of a UE comprises: training, based on historical samples, a regression model y using samples obtained from a set of parameters, wherein the regression model y comprises a function of a first term X and a second term h; and predicting, based on the regression model y, a target KPI to capture parameter impacts corresponding to the second term h.

Abstract: A training aid for shooting a basketball including a head-mounting portion configured to mount onto the head of a person shooting the basketball and a feedback-providing portion attached to, and supported by, the head mounting portion. The feedback-providing portion defining a forward-facing engagement surface extending upward from the head-mounting portion that provides tactile feedback to the person when the engagement surface is contacted by at least one of the basketball and a shooting hand of the person shooting the basketball.

Abstract: A method includes partitioning a set of configuration management (CM) data for one or more cellular network devices into multiple distinct time intervals, each time interval associated with a distinct set of CM settings at the one or more cellular network devices, the CM data comprising multiple CM parameters. The method also includes determining a regression model based on the set of CM data. The method also includes applying the regression model to compute a distinct set of scores and compare the set of scores to estimate whether a performance of the one or more cellular network devices has changed during a second time interval relative to a first time interval.

Abstract: A method for operating a base station is provided. The method includes in response to a triggering event, fetching information on a base station (BS) configuration parameters comprising a location, a height, an antenna pattern, and topographical details surrounding the BS; determining the BS configuration parameters that are error prone and require re-estimation; obtain measurement reports created by at least one user equipment (UE); determining an audit method to perform an audit correction, the audit correction based on the one or more of the BS configuration parameters to re-estimate, available BS information and the measurement reports; performing the audit correction, to obtain a result based on a computed score for each candidate value of the BS configuration parameters; generating, based on the result, one or more corrective actions; and adjusting at least one of the BS configuration parameters based on the one or more corrective actions.

Abstract: A core network entity (CNE) can predict received signal strength values for base station (BS). In response to a triggering event, the CNE fetches information on BS configuration parameters, including at least one of: a BS class, a BS location, a BS height, an orientation of the BS, a BS antenna pattern, and topographical details surrounding the BS. The BS obtains, processes and forwards to the CNE, measurement reports created by a user equipment (UE) including a signal strength value and a location of the UE. The CNE pools the measurement reports based on the BS class and, in response to another triggering event, recalibrates signal strength prediction tools, which can predict received signal strength values from the BS to a location in a vicinity of the BSs. The CNE also pools and stores the measurement reports and corresponding BS configuration parameters, after post processing and compression.

Abstract: One or more techniques and/or systems are disclosed for a myringotomy tube including a tube body having a first end and a second end. The tube body may comprise a shaft operably connecting the first end and the second end. The first end may comprise a first visual element. The second end may comprise a second visual element. The first visual element may be different than the second visual element. The myringotomy tube may be a single piece. The myringotomy tube may be multiple pieces.

Abstract: A training aid for shooting a basketball including a head-mounting portion configured to mount onto the head of a person shooting the basketball and a feedback-providing portion attached to, and supported by, the head mounting portion. The feedback-providing portion defining a forward-facing engagement surface extending upward from the head-mounting portion that provides tactile feedback to the person when the engagement surface is contacted by at least one of the basketball and a shooting hand of the person shooting the basketball.

Abstract: A core network entity (CNE) can predict received signal strength values for base station (BS). In response to a triggering event, the CNE fetches information on BS configuration parameters, including at least one of: a BS class, a BS location, a BS height, an orientation of the BS, a BS antenna pattern, and topographical details surrounding the BS. The BS obtains, processes and forwards to the CNE, measurement reports created by a user equipment (UE) including a signal strength value and a location of the UE. The CNE pools the measurement reports based on the BS class and, in response to another triggering event, recalibrates signal strength prediction tools, which can predict received signal strength values from the BS to a location in a vicinity of the BSs. The CNE also pools and stores the measurement reports and corresponding BS configuration parameters, after post processing and compression.

Abstract: A method for operating a base station is provided. The method includes in response to a triggering event, fetching information on a base station (BS) configuration parameters comprising a location, a height, an antenna pattern, and topographical details surrounding the BS; determining the BS configuration parameters that are error prone and require re-estimation; obtain measurement reports created by at least one user equipment (UE); determining an audit method to perform an audit correction, the audit correction based on the one or more of the BS configuration parameters to re-estimate, available BS information and the measurement reports; performing the audit correction, to obtain a result based on a computed score for each candidate value of the BS configuration parameters; generating, based on the result, one or more corrective actions; and adjusting at least one of the BS configuration parameters based on the one or more corrective actions.