Samsung Electronics Co., Ltd.

Apple Inc.

Amicus Wireless Technology Ltd

Qualcomm Incorporated

Other

Kee-Bong Song

Syed Aon Mujtaba

Pranav Dayal

Hongbing Cheng

Xiaowen Wang

Tarik Tabet

Hyukjoon Kwon

Youngjae Kim

Elina Nayebi

Vinay R Majjigi

Paul V Flynn

Hamed Maleki

Yuchul Kim

Yanru Tang

Qi Zhan

Johnson O Sebeni

Wen Zhao

William Burchill

Jung Hyun Bae

Won-Joon Choi

Vignesh Sethuraman

Navid Damji

Linbo Li

Hou-Shin Chen

Hairuo Zhuang

Jungwon Lee

Ahmed Attia Abotabl

Christian W Mucke

Sang Won Son

Xiaoqiang Ma

Federico Penna

Amir Dezfooliyan

Young Jae Kim

Farrokh Etezadi

Sung-En Chiu

Shailesh Chaudhari

Juan Montojo

Sharad Deepak Sambhwani

Dongwoon Bai

Karthik Anantharaman

Siu-Chuang Ivan Lu

Chaohuang Zeng

Niranjan Nayak Ratnakar

Tahir Shamim

Karthik Nagarajan

Shengshan Cui

Siuchuang Ivan Lu

Yangwen Liang

Jang Wook Moon

Seyed Reza Mir Ghaderi

Romain Trilling

Bofeng Yuan

Avneesh Agrawal

Li Su

Jianxiong Shi

Donghoon Lee

Christopher J Hansen

Zhu Ji

Sreevalsan Vallath

Dong-jun Lee

Hamid Saber

Haining Zhang

Fan Wang

Isabel Mahe

Bharath Narasimha Rao

Gennady Feygin

Chun Kin Au Yeung

Tienyu Chang

Ming Hu

Shuangquan Wang

Jinhong Wu

Ming Yan

S Aon Mujtaba

Gwang-Hyun Gho

Yacong Ding

Nitin Jonathan Myers

Pritesh Vora

Bemini Hennadige Janath Peiris

Kihyun Do

Charles Pandana

Seongho Hur

Jeong Heon Lee

Daeson Kim

Mohamad Monzer Mansour

Joyson Sebastian

Mehdi Ganji

William S Burchill

Karthik Anatharaman

A method and system for multiple-input multiple-output (MIMO) detector selection using a neural network is herein disclosed. According to one embodiment, a method includes generating a labelled dataset of features and detector labels, training a multi-layer perceptron (MLP) network using the generated labelled dataset, and selecting a detector class from a plurality of detector classes based on outputs of the trained MLP network.

Multiple-input multiple-output (MIMO) detector selection using neural network

Provided is a method of decoding, the method including receiving, by a user equipment (UE), a downlink control information (DCI) that is encoded, identifying, by the UE, a first bit position of the DCI as a known bit, and reducing a number of candidate code words for the DCI based on the known bit.

PDCCH decoding with known DCI bits

A method of compensating for IQ mismatch (IQMM) in a transceiver may include sending first and second signals from a transmit path through a loopback path, using a phase shifter to introduce a phase shift in at least one of the first and second signals, to obtain first and second signals received by a receive path, using the first and second signals received by the receive path to obtain joint estimates of transmit and receive IQMM, at least in part, by estimating the phase shift, and compensating for IQMM using the estimates of IQMM. Using the first and second signals received by the receive path to obtain estimates of the IQMM may include processing the first and second signals received by the receive path as a function of one or more frequency-dependent IQMM parameters.

Systems, methods and devices for joint calibration of transmit and receive IQ mismatch

Disclosed is an electronic device including a processor and memory, the processor being configured to perform frequency interpolation on a channel estimation at all resource elements (REs) located where a demodulation reference signal is transmitted, perform time interpolation on a frequency domain interpolated channel obtained from the frequency interpolation, and calculate an enhanced channel estimation based on channel estimates at REs in a frequency domain and REs in a time domain, the channel estimates being output from the time interpolation.

Enhancement of channel estimation in wireless communication based on supervised learning

A method of optimizing at least one IQMC parameter value for an IQMC includes: generating a set of tested IQMC candidate parameter values by performing an iterative method including selecting a first IQMC candidate parameter value for the at least one parameter of the IQMC; determining, using the first IQMC candidate parameter value, a performance metric value that comprises at least one of (i) an image rejection ratio (IRR) value, (ii) a signal-to-interference-plus-noise ratio (SINR) value, or (iii) a signal-to-image ratio (SImR) value; and determining a second IQMC candidate parameter value that is an update to the first IQMC candidate parameter value. The method of optimizing at least one IQMC parameter value for an IQMC further includes determining an IQMC candidate parameter value of the set of tested IQMC candidate parameter values that optimizes the performance metric.

Systems and methods for mitigating in-phase and quadrature mismatch

A system and method are provided in which an input including a measurement output of digital channel information is received. The digital channel information includes an estimated digital channel, a beamforming codebook, and quantized angle of arrival (AoA) values. A basis corresponding to one of the quantized AoA values is selected according to a largest projection power of the measurement output. A partial channel is recovered based on the beamforming codebook and the estimated digital channel. An analog channel is recovered based on the partial channel and the basis.

System and method for providing channel recovery for angle domain sparse channels

A method and system for use of carrier aggregation capability. In some embodiments, the method includes receiving, by a network, a declaration of capabilities, from a user equipment (UE), the declaration of capabilities including: a declaration of a first capability for a first component carrier (CC), and a declaration of a second capability for a second CC. The method may further include configuring, by the network, the UE with one or more CCs including the first CC and not including the second CC, the configuring including configuring the first CC with a configuration exceeding the first capability.

Methods and systems for use of carrier aggregation capability

A system and method for configuring an RF network based on machine learning. In some embodiments, the method includes: receiving, by a first neural network, a first state and a first state transition, the first state including: one or more identifiers for available active ports, and a set of available connections between two or more circuit elements, each of the circuit elements being one of: (1) a first circuit type, (2) a second circuit type that operatively connects a circuit element of the first circuit type to one of the available active ports, and (3) the available active ports; and generating, by the first neural network, a first estimated quality value, for the first state transition.

Hardware allocation in RFIC based on machine learning

A method and a closed-loop antenna impedance tuning (CL-AIT) system are provided. An input reflection coefficient is determined. The input reflection coefficient and a threshold value are compared to determine whether the input reflection coefficient is greater than the threshold value. In response to determining that the input reflection coefficient is greater than the threshold value, an optimal tuner code is determined based on a tuner code search algorithm. The optimal tuner code is applied to configure the CL-AIT system.

Data-driven methods for look up table-free closed-loop antenna impedance tuning

A closed-loop power control (CLPC) system is disclosed that includes a first signal path for a first polarization and a second signal path for a second polarization. The first signal path includes a first power amplifier, a first output power detector configured to detect a first output power level of the first power amplifier, and a first processor configured to determine a first analog gain for a first controller and a first gain for a first digital-to-analog converter based on a first accumulated error between the first output power level and a target Effective Isotropically Radiated Power. A second processor is configured to set a first variable gain of a first variable gain amplifier coupled to an input of the first power amplifier. The CLPC can be configured to control the gain of the first signal path separately or as one signal path.

Distributed closed-loop power control with VGA gain update

A method of controlling power in a transmission system may include determining a first transmit power of a first transmit path, determining a second transmit power of a second transmit path, and controlling the first transmit path and the second transmit path based on a combination of the first transmit power and the second transmit power. The combination of the first transmit power and the second transmit power may include a sum of the first transmit power and the second transmit power. Controlling the first transmit path and the second transmit path may include determining a first effective power target for the first transmit path based on the first transmit power and the second transmit power, and determining a second effective power target for the second transmit path based on the first transmit power and the second transmit power.

Systems, methods, and apparatus for combined power control of multiple transmit paths

A wireless communication device includes: a processing circuit configured to: receive, from an antenna array during a previous period, a first directional electromagnetic signal including beam sweeping reference symbols of a previous beam sweeping period; compute an estimated combined channel; estimate a dominant angle-of-arrival (AoA) of the first directional electromagnetic signal based on the estimated combined channel and a previous beamforming codebook including two or more beamforming vectors corresponding to different AoAs; construct an updated beamforming codebook based on the estimated dominant AoA and one or more remaining AoAs spaced apart from the estimated dominant AoA; receive, at the antenna array during a current period, a second directional electromagnetic signal including data symbols; determine a beamforming vector for data reception of the second directional electromagnetic signal based on the updated beamforming codebook; and detect the data symbols in the second directional electromagnetic signal based on the determined beamforming vector.

Systems and methods for updating beamforming codebooks for angle-of-arrival estimation using compressive sensing in wireless communications

A first antenna array includes antenna panels including: first antenna panels arranged on a first circle having a first radius, each of the first antenna panels including antenna elements; and second antenna panels arranged on a second circle having a second radius, each of the second antenna panels including antenna elements, the second circle being concentric with the first circle at a center point, the second antenna panels being arranged at a first angle around the center point with respect to the first antenna panels, the first radius, the second radius, and the first angle being computed in accordance with wireless transmission conditions including: a line-of-sight distance to a second antenna array including third antenna panels arranged on two or more circles; and a carrier frequency of a line-of-sight wireless transmission between the first antenna array and the second antenna array.

Systems and methods for antenna placement for wireless communications

A method for minimizing a time domain mean square error (MSE) of channel estimation (CE) includes estimating, by a processor, a power delay profile (PDP) from a time domain observation of reference signal (RS) channels; estimating, by the processor, a noise variance of the RS channels; and determining, by the processor, a first arrival path (FAP) value and a delay spread estimation (DSE) value based on the estimated PDP and the estimated noise variance for minimizing the MSE of CE.

System and method for providing first arrival path (FAP) and delay spread estimation (DSE) in wireless communication system

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San Diego, CA, United States of America

Kee-Bong Song