An apparatus and system to provide a federated learning scheme between a RAN and connected UEs are described. A gNB-DU, gNB-CU, or LMF acts as a central server that selects an AI/ML model, trains the AI/ML model, and transmits the AI/ML model to UEs. The UEs act as local nodes that each send a model request to the central server, receive the AI/ML model in response to the request, trains the AI/ML model locally with data, and report updated parameters to the central server. The central server aggregates parameters from the local nodes and updates the AI/ML model.

Systems, methods, apparatuses, and computer program products for machine learning based channel state information (CSI) estimation and feedback configuration are provided. A method may include learning channel state information feedback from one or more user equipment as time series data. The method may also include building a predictive model for user equipment feedback based on the learned channel state information feedback. The method may further include configuring a channel state information trigger with the one or more user equipment based on the predictive model. In addition, the method may include signaling predicted channel state information to the one or more user equipment.

Information about a signal device is received at a first location in a first physical environment. The signal device broadcasts a signal to a computing device. A first indication is received from the computing device. The first indication includes a first strength of signal of the signal device received by the computing device. Whether the first strength of signal is above a threshold is determined. A second location is determined. The second location is where the computing device is located when the first strength of signal is above the threshold. The second location is within the first physical environment. A first visual representation of the first physical environment is displayed. The first visual representation includes one or more of the following: the signal device at the first location, at least one physical item found in the physical environment, a broadcasting power of the signal device, and the second location.

Systems and methods for estimation and learning for optimization of a distributed Wi-Fi network performed by a cloud controller include obtaining data associated with operation of the distributed Wi-Fi network; processing the obtained data; determining one or more of forecasts, predictions, trends, and interference for the distributed Wi-Fi network based on the processed data; and performing an optimization of the distributed Wi-Fi network based on the determined one or more forecasts, predictions, trends, and interference. The obtained data can time-series data, and wherein the processing comprises collating the time-series data across all nodes in the distributed Wi-Fi network and segmenting the time-series data into time periods with similar load characteristics.

Systems and methods for autonomous signal modulation format identification are disclosed. In an example embodiment of the disclosed technology, a method includes mapping an input signal to Stokes space to generate a representation of the input signal in three-dimensional space. The method may further include determining the dimension of the representation and, based on the dimension, selecting a subset of modulation from a plurality of mutually exclusive subsets of modulation formats. Further, the method may include defining a cost function for identifying the modulation format from the selected subset and evaluating the cost function to identify the modulation format.

This disclosure provides a network call method and apparatus, a computer device, and a storage medium, and belongs to the field of audio data processing. The method includes: performing time-frequency transformation on an acquired audio signal, to obtain a plurality of pieces of frequency domain information of the audio signal; determining a target bit rate corresponding to the audio signal according to the plurality of pieces of frequency domain information; and encoding the audio signal based on the target bit rate, and performing a network call based on the encoded audio signal.

A communication network element may send to a plurality of wireless devices a base radio link priority model that provides as an output a first prioritization of radio links. The wireless devices may generate trained radio link priority models using machine learning based on one or more attempts to establish a communication link with the communication network. The communication network element may receive trained radio link priority models from one or more wireless devices, update the base radio link priority model, and send to the wireless devices an updated base radio link priority model that provides as an output a second prioritization of radio links.

Systems and methods include obtaining data associated with operation of a Wi-Fi network, via a network interface connected to the one or more processors; analyzing the obtained data to determine one or more of forecasts, predictions, trends, and interference associated with the Wi-Fi network based on the obtained data and correlations determined therein; and causing configuration of the Wi-Fi network based on the determined one or more forecasts, predictions, trends, and interference, wherein the configuration includes at least one of channel selection, bandwidth selection, topology selection of access points in the Wi-Fi network, and client associations with the access points.

A method of tracking a user position using a crowd robot, a tag device, and a robot implementing the same are disclosed, and the robot includes a controller, which cumulatively stores position information of a tag device, generates a moving route corresponding to the stored position information of the tag device, and corrects the position information of the tag device based on position estimation information of a crowd robot around the tag device sent from the tag device.

According to an aspect of the present disclosure, a method for transmitting, by a road side unit (RSU), a signal in a wireless communication system comprises: receiving, from another RSU, an infrastructure-to-infrastructure (I2I) signal for pairing; on the basis of the I2I signal for pairing, performing pairing with the other RSU; and on the basis of the pairing having failed, transmitting an infrastructure-to-vehicle (I2V) signal every preset period, wherein the I2I signal for pairing comprises information regarding a period change value associated with the preset period, and, on the basis of the pairing having succeeded, the I2V signal is transmitted every period into which the preset period has been changed on the basis of the period change value.