Samsung Electronics Co., Ltd.

Abhishek Sehgal

Hao Chen

Wenxun Qiu

Boon Loong Ng

Guanbo Chen

Khuong N Nguyen

Rebal Al Jurdi

Jianzhong Zhang

Vutha Va

Vishnu Vardhan Ratnam

Hao-Hsuan Chang

Madhukar Budagavi

Jianhua Mo

Yuming Zhu

Esmaeil Faramarzi

Namjoon Park

Indranil Sinharoy

Eric Earl Johnson

Matthew Tonnemacher

Jihoon Sung

Junyeop Jung

Nishith D Tripathi

In-sick Jung

Kuldeep Gill

John Wensowitch

Dong Li

Bilal Sadiq

A method includes receiving CSI data for at least one communication link between a first electronic device (ED1) and a Wi-Fi device in a 3D space. The method includes preprocessing the CSI data; and determining whether a motion of an object is detected within a threshold distance to the ED1 based on the preprocessed CSI data. The detection of the motion of the object corresponds to a determination that the object is present in the space. The detection of no motion of the object corresponds to a determination that the object is not present in the space. The method includes outputting at least one of: a first indicator indicating that the space is empty, in response to the determination that the object is not present in the space; or a second indicator indicating that the space is occupied, in response to the determination that the object is present in the space.

Presence detection and recognition with Wi-Fi

A method includes obtaining Wi-Fi channel state information (CSI) parameters measured at an anchor and a target device from Wi-Fi frames transmitted between the anchor and the target device, the CSI parameters comprising, for each of the anchor and the target device, frame transmission and reception times, a carrier phase of the CSI, a crystal offset factor, and a packet detection time error. The method also includes determining a round trip time (RTT) estimate or a relative change in the RTT estimate as a function of the CSI parameters. The method further includes estimating a range between the anchor and the target device as a function of the RTT estimate, or estimating a change in the range between the anchor and the target device as a function of the relative change in the RTT estimate.

Wi-Fi based fine ranging using carrier phase information

A method and apparatus for detecting network service types by grouping together applications that have similar latency requirement and data characteristics to form a service type. Machine learning algorithms may be used to detect the traffic pattern in the traffic stream by using features extracted from packet information and optionally additional sensor information. Subsequently, the output of the machine learning module may go through a post-processing process that may employ different techniques to use current prediction and past predictions to make a final decision about the network service type.

Methods and apparatus for detecting network services

A method includes determining one or more active slots and inactive slots in an ultra-wide band (UWB) ranging round. The method also includes determining multiple UWB silent periods in the ranging round based on an arrangement of the inactive slots. The method also includes determining a sleep-wake-up schedule that aligns with at least one selected silent period of the multiple UWB silent periods in the ranging round, the sleep-wake-up schedule to be used for Wi-Fi communication. The method also includes waking up a Wi-Fi station (STA) for the Wi-Fi communication during the at least one selected silent period, then going back to sleep according to the sleep-wake-up schedule.

Power saving for in-device coexistence between Wi-Fi and Ultra-Wide Band communication

A method includes obtaining statistical information of incoming network traffic. The method also includes using a state machine to classify the incoming network traffic as a traffic pattern based on the statistical information. The method further includes using the traffic pattern to adapt one or more Target Wakeup Time (TWT) parameters to optimize power consumption of a Wi-Fi station. The traffic pattern includes at least one of bursty traffic, random traffic, and stable traffic.

System and method for traffic type detection and Wi-Fi target wake time parameter design

A method includes receiving CSI frames among P CSI frames transmitted from another electronic device. The method includes estimating a CSI from each of the received CSI frames as an available CSI estimate. The method includes identifying an impairment model accounting for inclusion of amplitude or phase errors of an estimated CSI corresponding to a p-th CSI frame among the P CSI frames. The method includes compensating, via the impairment model, for the errors in amplitude and phase of P CSI estimates. Compensating for the errors in amplitude and phase of the P CSI estimates can be based on: approximating a missing CSI estimate based on the available CSI estimates; determining an AGC gain value; or computing the errors in the phase based on a weighted least-squares solution. The method includes estimating a breathing rate of a subject based on different spatial dimensions of the P compensated CSI estimates.

Multi-antenna WiFi based breathing rate estimation

A method includes obtaining input features based on network traffic received during a time window. The method also includes generating multiple network service type predictions about the network traffic during the time window using a machine learning (ML) classification system operating on the input features. The method also includes storing the multiple network service type predictions in different time steps in a first-in first-out (FIFO) buffer and generating decisions about a presence of each of multiple service types in the network traffic using a voting algorithm. The method also includes reducing fluctuations in the generated decisions using a logic-based stabilizer module to generate a final network service type decision.

System and method for detecting network services based on network traffic using machine learning

A method includes determining one or more active slots and inactive slots in an ultra-wide band (UWB) ranging round. The method also includes determining multiple silent periods in the ranging round based on an arrangement of the inactive slots. The method also includes determining a target silent period in the ranging round, among the multiple silent periods, to be used for WiFi communication. The method also includes determining multiple target wake time (TWT) parameters such that a TWT session period defined by the TWT parameters overlaps with the target silent period. The method also includes determining one or more second silent periods preceding the target silent period in which the TWT parameters can be negotiated with an access point (AP) for the WiFi communication. The method also includes negotiating the TWT parameters with the AP during the one or more second silent periods.

Apparatus and method for promoting in-device coexistence between Wi-Fi and ultra-wide band communication

A method for generating content includes receiving information regarding electronic devices respectively capturing content associated with an event. The method also includes identifying, based on the received information, one or more parameters for the electronic devices to use in capturing the content, the one or more parameters identified to assist in generating multi-view content for the event from the captured content. The method further includes identifying, based on the received information, a common resolution for the electronic devices to use in capturing the content. Additionally, the method includes identifying, based on the received information, a common frame rate for the electronic devices to use in capturing the content. The method also includes sending information indicating the one or more parameters, the common resolution, and the common frame rate to the electronic devices.

Generation and distribution of immersive media content from streams captured via distributed mobile devices

A wireless communication device comprising a processor configured to obtain first information on network conditions and second information on packets delivered to another communication device during a current target wake time (TWT) session, and to update a TWT service period (SP) duration for a future TWT session based on the first information and the second information.

Adaptive adjustment for target wake time duration configuration

Embodiments of the present disclosure provide methods and apparatuses for updating a target wake time (TWT) service period and interval. The apparatuses include a communication device comprising a transceiver and a processor. The transceiver is configured to transmit and receive higher layer data packets in a TWT operation during a time period. The processor is configured to determine, based on PHY data rates during the time period, effective higher layer data rates, estimate an initial data transceiving time based on the effective higher layer data rates and total lengths of the data packets, adjust the initial data transceiving time to obtain a higher layer data transceiving time based on an estimated network congestion level, an estimated re-transmission rate, and a total amount of TWT overhead during the time period, and determine a new TWT service period and interval based on the higher layer data transceiving time.

Apparatus and method for target wake time parameter design

An electronic device, a method, and computer readable medium are disclosed. The method includes transmitting radar signals via a radar transceiver. The method also includes identifying signals of interest that represent biometric information of a user based on reflections of the radar signals received by the radar transceiver. The method further includes generating an input based on the signals of interest that include the biometric information. The method additionally includes extracting a feature vector based on the input. The method also includes authenticating the user based on comparison of the feature vector to a threshold of similarity with preregistered user data.

Method and apparatus for biometric authentication using face radar signal

A method and electronic device for radar-based face authentication anti-spoofing for determining access to the electronic device. The electronic device includes a radar transceiver and at least one processor. The at least one processor is configured to transmit, via the transceiver, a first set of signals, generate a channel impulse response (CIR) based on receipt of reflections of the first set of signals, detect a first CIR tap in the CIR, determine a selection of CIR data based on the detected first CIR tap, determine a profile matching metric based on comparison of the selection of CIR data to a set of predetermined reference signals, and determine whether to allow access to the electronic device based on comparison of the profile matching metric to a profile matching threshold.

Systems and methods for radar based face authentication anti-spoofing

A method of an electronic device for a connection management is provided. The method comprises: establishing a communication link with an access point (AP); receiving a triggering indication based on link information measured by the electronic device; determining a quality of the communication link between the electronic device and the AP based on the received triggering indication; comparing, based on the determined quality of the communication link, at least two Q values fetched from a Q table that is determined according to context in which in the electronic device is being used; setting a quantized aggressive index (QAI) value based on the compared at least two Q values; and generating a disconnection command based on the QAI value, wherein the disconnection command is a physical disconnection command or a virtual disconnection command of the communication link between the electronic device and the AP.

Method and apparatus for intelligent WiFi connection management

An electronic device, a method and computer readable medium for multimodal authentication are disclosed. The method includes receiving a request for authentication, facial signature data generated based on an input from a radar source of the electronic device, and facial image data generated based on an input from a camera of the electronic device. The method also includes identifying an illumination condition and a sensor condition associated with the electronic device. The method additionally includes assigning a weight associated with the camera and the radar source based on the illumination condition and the sensor condition. The method further includes granting the request when at least one of the facial signature data and the facial image data are within a threshold of similarity with a preregistered facial data associated with the weight.

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Richardson, TX, United States of America

Abhishek Sehgal