Abstract: A user equipment (UE) includes a transceiver, and a processor operatively coupled to the transceiver. The processor is configured to detect an occurrent of an event, and determine whether the event is a qualifying event. The processor is also configured to, in response to a determination that the event is a qualifying event, identify, based on a band map and a present location of the UE, an event improvement procedure, and perform the event improvement procedure.

Abstract: A UE includes a transceiver. The transceiver is configured to perform network activity for the UE. The UE further includes a processor operably coupled to the transceiver. The processor is configured to determine whether a change in network activity for the UE exceeds a metric, and upon determination that the metric is exceed, initiate an anomalous event detection process, and determine whether an anomalous event is detected. The processor is further configured to, upon detection of an anomalous event, determine a network load level, and modify, based on the determined network load level, a connection configuration of the UE.

Abstract: A method includes obtaining a gesture sample via a gesture classifier. The gesture sample includes parameters of features extracted from the radar signals based on user motion in performance of a gesture. The method includes determining whether to obtain feedback for a target gesture associated with the gesture sample. The method includes in response to a determination to obtain the feedback, obtaining the feedback for the target gesture. The feedback includes a label indicating a type of gesture. The method includes determining whether the gesture sample associated with the label is valid based on comparison to a dataset of pre-existing validated gesture samples and a distance threshold. The method includes in response to determining that the gesture sample associated with the label is valid, determining whether to update a model for the gesture classifier using the validated gesture sample as a training sample.

Abstract: A method of operating a UE includes receiving and transmitting traffic, over a time step, based on a first set of RF parameters received from a NW device. The method further includes classifying the traffic received and transmitted over the time step into a traffic class, selecting a second set of RF parameters based on the traffic class, estimating a throughput demand and a throughput supply, and determining, based on the estimated throughput demand and the estimated throughput supply, whether an anomalous traffic condition has occurred. The method further includes, if an anomalous traffic condition has not occurred, transmitting a request to the NW device to configure the UE with the second set of RF parameters, and if an anomalous traffic condition has occurred, transmitting a request to the NW device to configure the UE with a third set of RF parameters selected to alleviate the anomalous traffic condition.

Abstract: A method includes determining a preferred configuration of a user equipment (UE) based on side information about the UE. The method also includes sending the preferred configuration of the UE to a base station via a UE assistance information (UAI) framework. The method further includes receiving a new configuration of the UE from the base station after the base station determines the new configuration based on the preferred configuration. The method also includes configuring the UE according to the new configuration.

Abstract: An electronic device includes a transceiver configured to transmit and receive radar signals. The electronic device further comprises a processor operatively coupled to the transceiver. The processor is configured to adjust a field of view (FoV) associated with the transceiver based on a plurality of radar frames corresponding to the radar signals, extract a plurality of feature vectors from the plurality of radar frames based on the adjusted FoV. The processor is further configured to identify an activity based on the plurality of feature vectors, perform a gesture magnitude operation, and perform an action based on the gesture and a result of the gesture magnitude operation.

Abstract: Apparatuses and methods for traffic type based quality of experience (QoE) maintenance for mobile devices. A user equipment (UE) includes a transceiver. The transceiver is configured to receive and transmit traffic over a link with a wireless network. The UE further includes a processor operably coupled to the transceiver. The processor is configured to classify the traffic into at least one of real time (RT) traffic or non-real-time (NRT) traffic, generate a link deterioration prediction, select, based on the link deterioration prediction and the traffic class, a QoE maintenance action, and perform the QoE maintenance action.

Abstract: A user equipment (UE) includes a transceiver. The transceiver is configured to receive and transmit traffic, over a time step, via a wireless network. The UE further includes a processor. The processor is configured to determine a plurality of statistical features for the traffic received and transmitted over the time step, classify the traffic received and transmitted over the time step into a traffic class based on the statistical features and a traffic classification operation, determine a link condition, and select, based on the traffic class and the link condition, a set of preferred radio frequency (RF) parameters from a table. The transceiver is further configured to transmit UE assistance information (UAI) to the wireless network corresponding with the selected set of preferred RF parameters.

Abstract: An electronic device includes a transceiver configured to transmit and receive radar signals and a processor operatively coupled to the transceiver. The processor is configured to identify, based on the received radar signals, a plurality of radar frames related to an activity of a target. The processor is further configured to extract a plurality of features from the plurality of radar frames, compute burst attributes for the extracted features, predict a gesture based on the burst attributes, determine whether the predicted gesture is a valid gesture, and if the predicted gesture is a valid gesture, perform an action corresponding to the predicted gesture.

Abstract: An electronic device includes a transceiver configured to transmit and receive radar signals, and a processor operatively coupled to the transceiver. The processor is configured to extract a plurality of feature vectors from a plurality of radar frames corresponding to the radar signals, identify an activity based on the plurality of feature vectors, and determine whether the identified activity corresponds with a non-gesture. The processor is further configured to, if the activity fails to correspond with a non-gesture, identify a gesture that corresponds with the activity, and perform an action corresponding with the identified gesture.

Abstract: A method for end-to-end dynamic gesture recognition using mmWave radar is provided. The method includes triggering an electronic device to activate a gesture recognition mode in response to detecting that a condition for activating the gesture recognition mode is satisfied. The method includes obtaining radar data while the gesture recognition mode is activated, wherein the radar data includes time-velocity data (TVD). The method includes detecting a start and an end of a gesture based on the TVD of the obtained radar data. To classify the gesture, the method includes determining a gesture, from among a set of gesture, that corresponds to a portion of the TVD between the start and the end of the gesture. The method includes outputting an event indicator indicating that a user of the electronic device performed the gesture classified.

Abstract: A UE includes a processor configured to detect a type of service currently active at the UE based on a traffic history of the UE, determine UE status and sensor information, and determine a current communication configuration for the UE. The processor is further configured to, based on the service type, the UE status and sensor information, and the current communication configuration, predict a risk of the UE overheating; and based on the predicted risk of the UE overheating, identify a new communication configuration to propose for use by the UE. The UE further includes a transceiver operably coupled to the processor, the transceiver configured to transmit, to a BS, information indicating the new communication configuration.

Abstract: A method for warning a wearer of a pacemaker against potential electromagnetic interference by a radar-aided mobile device is provided. The method includes obtaining sensor data from a sensor of an electronic device. The method includes detecting electromagnetic interference (EMI) risk based on the sensor data obtained. The method includes determining whether a condition for executing an EMI risk mitigation function is satisfied, based on the detected EMI risk. The method includes, in response to determining that the condition for executing the EMI risk mitigation function is satisfied, performing the EMI risk mitigation function. In some embodiments, performing the EMI risk mitigation function includes at least one of: adjusting a radio frequency (RF) transmit power; and outputting an alert that warns a user of the electronic device about a pacemaker in relation to the detected EMI risk.

Abstract: A method includes obtaining a stream of radar data into a sliding input data window composed of recent radar frames from the stream. Each radar frame within the data window includes features selected from a predefined feature set and at least one of time-velocity data or time angle data. The method includes, for each radar frame within the data window, receiving a binary prediction indicating whether the radar frame includes a gesture end. The method includes in response to the binary prediction indicating that the radar frame includes the gesture end, triggering an early stop (ES) checker to determine whether an ES condition is satisfied. Determining whether the ES condition is satisfied comprises determining whether a noise frames condition and a valid activity condition are satisfied. The method includes in response to a determination that the ES condition is satisfied, triggering a gesture classifier to predict a gesture type.

Abstract: A method includes obtaining a target distance and target velocity for each radar frame within a sliding input data window. Each radar frame within the data window includes extracted features. The method includes determining a dynamic threshold distance (dth) for a range of distances wherein performance of a gesture is valid. The method includes determining whether the target distance corresponding to a current radar frame satisfies a proximity condition based on the dth. The method includes in response to a determination the proximity condition is not satisfied, detecting a start of activity based on the extracted features. The method includes segmenting gesture frames from non-gesture frames in the data window, in response to at least one of: a determination the first proximity condition is satisfied, or a determination the current radar frame includes an end of the activity. The method includes discarding the non-gesture frames to modify the data window.

Abstract: A method includes, during operation of a proximity-detection module of an electronic device: determining a distance, velocity, and angle of a target relative to the electronic device based on radar data obtained from a radar transceiver of the electronic device; adjusting a configuration of the radar transceiver based on at least one of the distance, velocity, or angle of the target; and while at least one of the distance, velocity, or angle is greater than a respective threshold, repeating the determining and adjusting operations. The method also includes, in response to determining that each of the distance, velocity, and angle is less than its respective threshold, determining whether at least one wake-up trigger for gesture-based commands is detected. The method further includes, in response to determining that the at least one wake-up trigger for gesture-based commands is detected, triggering operation of a gesture-recognition module of the electronic device.

Abstract: A method for end-to-end dynamic gesture recognition using mmWave radar is provided. The method includes triggering an electronic device to activate a gesture recognition mode in response to detecting that a condition for activating the gesture recognition mode is satisfied. The method includes obtaining radar data while the gesture recognition mode is activated, wherein the radar data includes time-velocity data (TVD). The method includes detecting a start and an end of a gesture based on the TVD of the obtained radar data. To classify the gesture, the method includes determining a gesture, from among a set of gesture, that corresponds to a portion of the TVD between the start and the end of the gesture. The method includes outputting an event indicator indicating that a user of the electronic device performed the gesture classified.