Qualcomm Incorporated

Kneron (taiwan) Co., Ltd.

Kneron, Inc.

Jie Wu

Dominic Gerard Farmer

Emilija Milorad Simic

Douglas Neal Rowitch

Rayman Wai Pon

Guttorm Ringstad Opshaug

Naga Bhushan

Stephen William Edge

Sven Fischer

Zoltan F Biacs

Marc Anthony Ische

Ardalan Heshmati

Srigouri Kamarsu

Ahmad Hatami

Daniel Fred Filipovic

William J Morrison

Lalitaprasad Daita

Liang Zhao

Wyatt Thomas Riley

Krishnaranjan Rao

Vishal Agarwal

Duong Hoang

Parvathanathan Subrahmanya

Jordan C Cookman

Amit Mahajan

Chun-Hao Hsu

Nitin Pant

Vieri Vanghi

Chun-Chen Liu

Jeffrey Wong

Junjie Su

Bike Xie

Robert Thrasher

Tong Lin

David Loweth Winslow

Hsiang-Tsun Li

Gengsheng Zhang

Joseph Czompo

Ashok Bhatia

Benjamin A Werner

Grant Alexander Marshall

Timothy Paul Pals

Mariam Motamed

Borislav Ristic

Rizwan Ehtesham Ahmed

Li Du

Yuan Du

Ming-Zhe Jiang

Jun-Jie Su

Bodong Zhang

Tianqi Wang

John Delucchi

Ravikumar Varma Dandu

Donovan Trisko

Yunhan Ma

Lalitaprasad V Daita

William James Morrison

Krishnaranjan S Rao

Disclosed are techniques for determining round-trip times (RTTs) between a user equipment (UE) and multiple base stations. In an aspect, the UE transmits an RTT measurement signal whose arrival time is measured by each of the base stations, and each of the base stations returns an RTT response signal whose arrival times are measured by the UE. In another aspect, the base stations each transmit an RTT measurement signal and the UE returns an RTT response signal. The receiver of the RTT measurement signal may include the measured arrival time in a payload of the RTT Response signal. Alternatively, the measured arrival time(s) of the RTT Measurement signal(s) and the transmission time(s) of the RTT Response signal(s) are sent in a separate message. The RTT signals can be wideband signals using low reuse resources.

Systems and methods for multiple round trip time (RTT) estimation in wireless networks

The subject matter disclosed herein relates to determining a background location of a mobile device using one or more signal metrics.

On demand positioning

A method of triggering an action includes: determining that a mobile device is under water by: determining that an amplitude of a satellite positioning system (SPS) signal, or a wireless communication signal, or a combination thereof, received by the mobile device is below a threshold amplitude; or comparing data from a motion sensor of the mobile device with motion sensor data characteristic of entering water; or a combination thereof; determining that the mobile device has an action-inducing relationship to water; and taking the action in response to determining that the mobile device has the action-inducing relationship to water.

Swimming activity monitoring

After inputting input data to a floating pre-trained convolution neural network to generate floating feature maps for each layer of the floating pre-trained CNN model, a statistical analysis on the floating feature maps is performed to generate a dynamic quantization range for each layer of the floating pre-trained CNN model. Based on the obtained quantization range for each layer, the proposed quantization methodologies quantize the floating pre-trained CNN model to generate the scalar factor of each layer and the fractional bit-width of a quantized CNN model. It enables the inference engine performs low-precision fixed-point arithmetic operations to generate a fixed-point inferred CNN model.

Low precision and coarse-to-fine dynamic fixed-point quantization design in convolution neural network

System and method for temperature-calibration of a crystal oscillator (XO) in a mobile device. A temperature-calibration status of the XO is determined and a trigger condition related to temperature-calibration of the XO is detected. If the temperature-calibration status of the XO is not fully temperature-calibrated or if the XO has not been previously temperature-calibrated, a temperature-calibration session is initiated by an XO manager based on the condition, wherein a receiver is configured to receive signals and temperature-calibration of the XO is performed in a background mode based on the received signals. The condition based triggering ensures that the XO is temperature-calibrated prior to launch of any position based or global navigation satellite systems (GNSS) based applications on the mobile device. The trigger condition can include first use or power-on, charging, presence in an outdoor environment, variation in operating temperature, pre-specified time, and/or user input pertaining to the mobile device.

Background oscillator calibration

Methods and systems for wireless communication are provided. In one example, a method comprises: receiving, by a mobile device, a radio beam, the radio beam being a directional beam that propagates along an angle of departure with respect to an antenna that transmits the radio beam; identifying, by the mobile device, at least one of: the radio beam or a base station that operates the antenna; determining, by the mobile device, a position of the mobile device based on identifying at least one of the radio beam or the antenna of the base station; and outputting, by the mobile device, the position of the mobile device.

Navigation and positioning system using radio beam

A wireless communication system includes: a first sub-system including a first radio and a second radio, each including a transceiver or a receiver; and a second sub-system coupled to the first sub-system and including: a first antenna configured to convey signals; a first tuner coupled to the first antenna; and a controller coupled to the first tuner, the controller being configured to cause the first tuner to adjust a characteristic of the first tuner; where the controller and/or the first tuner is configured to provide, to the first sub-system, an indication of an effect on operational performance of a portion of the second sub-system coupled to the second radio based on operation of the first tuner or the first antenna, or a combination thereof; where the first sub-system is configured to modify operation of the second radio based on the indication of the effect on operational performance.

Radio control based on operational performance feedback

Methods, apparatuses, and computer-readable media are described. In one example, a method, on a base station, for providing position measurements signals in a wireless communication network, comprises: determining a plurality of subcarriers for downlink transmission, wherein the plurality of subcarriers for downlink transmission comprise all subcarriers indicated in a resource block of a scheduled time of transmission within a scheduled transmission occasion, wherein the resource block comprises a plurality of symbol periods, wherein each symbol period of the plurality of symbol periods is for transmission of a symbol using one or more subcarriers of the plurality of subcarriers; and transmitting at the scheduled time of transmission, and using each subcarrier of the plurality of subcarriers, a wireless position measurement signal at the scheduled transmission occasion, the wireless position measurement signal being part of a sequence of wireless signals representing a position measurement signal bitstream.

Signal structure for navigation and positioning signals

A system for operating a floating-to-fixed arithmetic framework includes a floating-to-fix arithmetic framework on an arithmetic operating hardware such as a central processing unit (CPU) for computing a floating pre-trained convolution neural network (CNN) model to a dynamic fixed-point CNN model. The dynamic fixed-point CNN model is capable of implementing a high performance convolution neural network (CNN) on a resource limited embedded system such as mobile phone or video cameras.

Deep neural network with low-precision dynamic fixed-point in reconfigurable hardware design

A method of monitoring swimming activity includes: determining at least one of an actual relationship of a mobile device to water or an expected relationship of the mobile device to water; determining ranges to satellites, based on signals received by a satellite positioning system (SPS) receiver of the mobile device, in response to the signals being received by the SPS receiver when the at least one of the actual relationship of the mobile device to water or the expected relationship of the mobile device to water is a desired relationship of the mobile device to water; and determining a location of the mobile device based on the ranges to the satellites.

A method of compressing a pre-trained deep neural network model includes inputting the pre-trained deep neural network model as a candidate model. The candidate model is compressed by increasing sparsity of the candidate, removing at least one batch normalization layer present in the candidate model, and quantizing all remaining weights into fixed-point representation to form a compressed model. Accuracy of the compressed model is then determined utilizing an end-user training and validation data set. Compression of the candidate model is repeated when the accuracy improves. Hyper parameters for compressing the candidate model are adjusted, then compression of the candidate model is repeated when the accuracy declines. The compressed model is output for inference utilization when the accuracy meets or exceeds the end-user performance metric and target.

Self-tuning incremental model compression solution in deep neural network with guaranteed accuracy performance

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San Diego, CA, United States of America

Jie Wu