Samsung Electronics Co., Ltd.

Marvellworld Trade Ltd.

Hyundai Motor Company

Kia Corporation

Hyukjoon Kwon

Jungwon Lee

Hongyuan Zhang

Sudhir Srinivasa

Kee-Bong Song

Raja Banerjea

Inyup Kang

Rohit U Nabar

Linbo Li

Yong Liu

Yacong Ding

Sandeep H Krishnamurthy

Dongwoon Bai

Niranjan Nayak Ratnakar

Farrokh Etezadi

Shailesh Chaudhari

Jung Hyun Bae

Yong Uk Shin

Federico Penna

Qi Zhan

Woojun Choi

Hyeyoung Sheen

Hongbing Cheng

Xiaoqiang Ma

Mojtaba Rahmati

Jooyeol Yang

Mu-Sheng Lin

Junho Lee

Hojung Lee

Jaein Kim

Amir Dezfooliyan

Vignesh Sethuraman

Hamed Maleki

Hyungil Lee

Nitin Jonathan Myers

Abdulrahman Baknina

Hui Won Je

Ahmed Elshafiy

Mouna Hajir

Ramin Soltani

Yanlin Zhou

Ramy E Ali

Mohamed Mahmoud

Methods and an apparatus are provided. A method includes generating an intermediate symbol time recovery (STR) adjustment based on a difference between an estimated first arrival path (FAP) and an FAP offset and generating an accumulated STR adjustment based on at least the intermediate STR adjustment and a feedback STR adjustment.

Apparatus and method for symbol time recovery using feedback loop

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

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 and system are provided. The method includes performing channel estimation on a reference signal (RS), compressing, with a neural network, the channel estimation of the RS, decompressing, with the neural network, the compressed channel estimation, and interpolating the decompressed channel estimation.

Deep learning-based channel buffer compression

A method may include determining, for a channel, a target component of precoding information, determining at least a part of the precoding information based on the target component and a decompressed component of the precoding information, and sending, from a user equipment, the at least a part of the precoding information. Determining the at least a part of the precoding information may be based on a correlation between the target component and the decompressed component. The at least a part of the precoding information may include a vector of coefficients. The vector of coefficients may include a vector of complex numbers. At least one of the complex numbers may include an amplitude representing a scaling coefficient and a phase representing a rotation coefficient. The target component may include a target matrix. The decompressed component may include a decompressed matrix. The target matrix may include a linear combination coefficients matrix. The precoding information may include a precoding matrix indicator.

Systems, methods, and apparatus for determining precoding information for beamforming

A method and an apparatus are provided in which a vector is generated from a covariance matrix of a received signal. The vector is input to a neural network to obtain an enhanced vector. The enhanced vector is converted into an enhanced matrix. Interference whitening is performed on the received signal using a whitening filter based on the enhanced matrix.

Method and apparatus for matrix enhancement in MIMO receiver with deep learning

Methods and apparatus are provided in which channel matrices at time slots are estimated using reference signals received from a base station (BS) at the time slots. A sequence of channel matrices at future time slots are estimated using the estimated channel matrices as input to a neural network (NN) trained based on known sets of past and future channel matrices. A parameter is determined using at least one channel matrix from the sequence of channel matrices.

Method and apparatus for learning-based channel matrix prediction

A method of modifying a common phase error (CPE) estimate of a slot including symbols, the method including receiving a CPE value corresponding to a symbol of a slot by an artificial neural network, generating a modified CPE value with the artificial neural network, and outputting the modified CPE value from the artificial neural network.

Learning-based common phase error estimation

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

The disclosure provides a method of providing implicit channel state information (CSI) feedback from a user equipment (UE). The method includes determining a precoding matrix indicator (PMI) selection decision metric, selecting one of a sub-band (SB) linear combination coefficient (LCC) selection method, a wideband (WB) LCC selection method, or a sub-band group (SBG) LCC selection method, based on the determined PMI selection decision metric, determining, using the selected LCC selection method, PMI indices based on sub-bands configured by a base station, and transmitting the determined PMI indices to the base station.

Method and apparatus for selection of linear combination coefficients for precoding in frequency-selective channels

A system and a method are disclosed for normalizing Log-Likelihood Ratios for bits of a transport block. For a narrowband channel estimation (NBCE), a normalization factor is selected based on an estimated delay spread, a cyclic prefix, an estimated Doppler spread, a rank, a modulation and coding scheme (MCS) and a signal-to-noise ratio (SNR) for the transport block, and an input LLRfor the individual bits of the transport block are scaled to respectively form an output LLRfor the individual bits of the transport block using the normalization factor. For a wideband channel estimation, a normalization factor is selected based on the rank/MCS/SNR of the transport block, and the input LLRfor the individual bits of the transport block are scaled to respectively form the output LLRfor the individual bits of the transport block using the normalization factor.

Method and apparatus for normalization of softbits based on channel characteristics

A method may include generating an estimated time offset based on a reference signal in a communication system, and adjusting a transform window in the communication system based on the estimated time offset, wherein the estimated time offset is generated based on machine learning. Generating the estimated time offset may include applying the machine learning to one or more channel estimates. Generating the estimated time offset may include extracting one or more features from one or more channel estimates, and generating the estimated time offset based on the one or more features. Extracting the one or more features may include determining a correlation between a first channel and a second channel. The correlation may include a frequency domain correlation between the first channel and the second channel. Extracting the one or more features may include extracting a subset of a set of features of the one or more channel estimates.

Systems, methods, and apparatus for symbol timing recovery based on machine learning

A method and system for training a neural network are herein provided. According to one embodiment, a method includes generating a first labelled dataset corresponding to a first modulation scheme and a second labelled dataset corresponding to a second modulation scheme, determining a first gradient of a cost function between a first neural network layer and a second neural network layer based on back-propagation using the first labelled dataset and the second labelled dataset, and determining a second gradient of the cost function between the second neural network layer and a first set of nodes of a third neural network layer based on back-propagation using the first labelled dataset. The first set of nodes of the third neural network layer correspond to a first plurality of detector classes associated with the first modulation scheme.

A hybrid power transmission apparatus for a vehicle is disclosed. The transmission includes an engine and first and second motor-generators as power sources. The transmission may include: a planetary gear set configured to include a first rotation element that is operatively connected to the first motor-generator, a second rotating element that is operatively connected to the engine and operatively connected to a first intermediate shaft, and a third rotation element to which a second intermediate shaft is operatively connected; a synchronizer configured to optionally operatively connect the second motor-generator to the first intermediate shaft, the second intermediate shaft, or both the first and second intermediate shafts; and an output shaft operatively connected to the second intermediate shaft to output power.

Power transmission apparatus of hybrid electric vehicle

A method and an apparatus in a multiple-input multiple-output (MIMO) wireless communication system are provided. A signal is received over a channel. The channel is decomposed into a plurality of virtual channels. Symbol detection is performed on each of the plurality of virtual channels. Values are obtained for the channel. Decoding is performed using the values to output a decoded symbol value of the received signal.

Method and apparatus for high rank multiple-input multiple-output (MIMO) symbol detection

Growing community of inventors

San Diego, CA, United States of America

Hyukjoon Kwon