Samsung Electronics Co., Ltd.

Qualcomm Incorporated

University of California

Inyup Kang

Jungwon Lee

Mostafa El-Khamy

Gilbert Christopher Sih

Hyukjoon Kwon

Qiuzhen Zou

Matthew Levi Severson

Tao Li

Jung Hyun Bae

Sungsoo Kim

Hessam Mahdavifar

Jinhong Wu

Heejin Roh

Dongwoon Bai

Christian Holenstein

Paul E Peterzell

Gennady Feygin

Arun Raghupathy

Shahab Sanayei

Raghu Narayan Challa

Brian Clarke Banister

Brett Christopher Walker

Yoo Jin Choi

Yingqun Yu

Insoo Hwang

Vignesh Sethuraman

Vladislav Sorokine

Li Feng Zhang

Haitao Zhang

Farrokh Abrishamkar

Christopher Patrick

Way-Shing Lee

Arvind Yedla

Byonghyo Shim

Jian Wei

Deepu John

Hanfang Pan

Mark Roh

Hairuo Zhuang

Ian J Fevrier

Brendon Lewis Johnson

James Krysl

Quaeed Motiwala

Tiangao Gou

Jooyeol Yang

Bruce R Meagher

Chihtung Chen

Hyunsang Cho

Kwangman Ok

Mostafa El Khamy

Viraphol Chaiyakul

Junghyun Bae

Hanju Kim

Karthikeyan Ethirajan

Avneesh Agrawal

Shimman Arvind Patel

Sharad Deepak Sambhwani

Jaeyoung Kim

Zhu Ji

Parvathanathan Subrahmanya

Daniel Fred Filipovic

Michael Lee McCloud

Supratik Bhattacharjee

Jong Hyeon Park

Je Woo Kim

Arash Mirbagheri

Linbo Li

William Robert Gardner

Hongbing Cheng

Sang-Hyuck Ha

Manoj Sachdev

Joseph Cheung

Thomas Wilborn

Sanjay K Jha

Matthias Brehler

Arthur James Neufeld

Jaber Mohammad Borran

Amr M Fahim

Junseok Park

Ju Won Park

Hsien-Ping Lin

Thomas Domenick Marra

Thunyachate Ekvetchavit

Chun Kin Au Yeung

Soon-Seng Lau

Jian Lin

Richard Higgins

Marcel Nassar

Rajesh Sundaresan

Pillappakkam Bahukutumbi Srinivas

Charles E Sakamaki

Julio Arceo

Soobok Yeo

Srinivas Varadarajan

Mohamed Elgebaly

Mark D Levin

Prashant A Kantak

Gregory Foerster

Mu-Sheng Lin

Reza Kalbasi

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

Apparatuses (including user equipment (UE) and modern chips for UEs), systems, and methods for UE downlink Hybrid Automatic Repeat reQuest (HARQ) buffer memory management are described. In one method, the entire UE DL HARQ buffer memory space is pre-partitioned according to the number and capacities of the UE's active carrier components. In another method, the UE DL HARQ buffer is split between on-chip and off-chip memory so that each partition and sub-partition is allocated between the on-chip and off-chip memories in accordance with an optimum ratio.

Method and system for contiguous HARQ memory management with memory splitting

An neural network (NN) processor includes an input feature map buffer configured to store an input feature matrix, a weight buffer configured to store a weight matrix trained in a form of a, a transform circuit configured to perform a Walsh-Hadamard transform on an input feature vector obtained from the input feature matrix and a weight vector included in the weight matrix to output a transformed input feature vector and a transformed weight vector, and an arithmetic circuit configured to perform an element-wise multiplication (EWM) on the transformed input feature vector and the transformed weight vector.

Neural network processor using dyadic weight matrix and operation method thereof

A method, system, and non-transitory computer-readable recording medium of decoding a signal are provided. The method includes receiving signal to be decoded, where signal includes at least one symbol; decoding signal in stages, where each at least one symbol of signal is decoded into at least one bit per stage, wherein Log-Likelihood Ratio (LLR) and a path metric are determined for each possible path for each at least one bit at each stage; determining magnitudes of the LLRs; identifying K bits of the signal with smallest corresponding LLR magnitudes; identifying, for each of the K bits, L possible paths with largest path metrics at each decoder stage for a user-definable number of decoder stages; performing forward and backward traces, for each of the L possible paths, to determine candidate codewords; performing a Cyclic Redundancy Check (CRC) on the candidate codewords; and stopping after a first candidate codeword passes the CRC.

System and methods for low complexity list decoding of turbo codes and convolutional codes

Method for decoding signal includes receiving signal, where signal includes at least one symbol; decoding signal in stages, where each at least one symbol of signal is decoded into at least one bit per stage, wherein Log-Likelihood Ratio (LLR) for each at least one bit at each stage is determined, and identified in vector L; performing Cyclic Redundancy Check (CRC) on L, and stopping if Lpasses CRC; otherwise, determining magnitudes of LLRs in L; identifying K LLRs in Lwith smallest magnitudes and indexing K LLRs as r={r(1), r(2), . . . , r(K)}; setting Lto maximum magnitude of LLRs in Lor maximum possible LLR quantization value; setting v=1; generating {tilde over (L)}(r(k))=L(r(k))&#x2212;Lvsign[L(r(k))], for k=1, 2, . . . , K; decoding with {tilde over (L)}to identify {tilde over (L)}, wherein {tilde over (L)}is LLR vector; and performing CRC on {tilde over (L)}, and stopping if {tilde over (L)}passes CRC or v=2; otherwise, incrementing v and returning to generating {tilde over (L)}(r(k)).

Apparatuses (including user equipment (UE) and modem chips for UEs), systems, and methods for UE downlink Hybrid Automatic Repeat reQuest (HARQ) buffer memory management are described. In one method, the entire UE DL HARQ buffer memory space is pre-partitioned according to the number and capacities of the UE's active carrier components. In another method, the UE DL HARQ buffer is split between on-chip and off-chip memory so that each partition and sub-partition is allocated between the on-chip and off-chip memories in accordance with an optimum ratio.

A computing system includes: a sensor configured to receive an input observation including a sample count for processing an original content; and a control unit, coupled to the sensor, configured to: generate a covariance estimate based on the input observation for identifying the original content associated with the input observation for implementing a linear estimation mechanism, and calculate a weight combination based on the covariance estimate for identifying the original content.

Computing system with correlation estimation mechanism and method of operation thereof

A method and apparatus are provided. The method includes configuring a plurality of long short term memory (LSTM) networks, wherein each of the plurality of LSTM networks is at a different network layer, configuring a plurality of memory cells in a spatial domain of the plurality of LSTM networks, configuring the plurality of memory cells in a temporal domain of the plurality of LSTM networks, controlling an output of each of the plurality of LSTM networks based on highway connections to outputs from at least one previous layer and at least one previous time of the plurality of LSTM networks, and controlling the plurality of memory cells based on highway connections to memory cells from the at least one previous time.

System and method for higher order long short-term memory (LSTM) network

A method and system for decoding a signal are provided. The method includes receiving a signal, where the signal includes at least one symbol; decoding the signal in stages, where each at least one symbol is decoded into at least one bit per stage, wherein a Log-Likelihood Ratio (LLR) and a path metric are determined for each possible path for each at least one bit at each stage; determining the magnitudes of the LLRs; identifying K bits of the signal with the smallest corresponding LLR magnitudes; identifying, for each of the K bits, L possible paths with the largest path metrics at each decoder stage for a user-definable number of decoder stages; performing forward and backward traces, for each of the L possible paths, to determine candidate codewords; performing a Cyclic Redundancy Check (CRC) on the candidate codewords, and stopping after a first candidate codeword passes the CRC.

A method, apparatus, and non-transitory computer-readable recording medium for generating an algebraic Spatially-Coupled Low-Density Parity-Check (SC LDPC) code are provided. The method includes selecting an LDPC block code over a finite field GF(q) with a girth of at least 6; constructing a parity-check matrix H from the selected LDPC block code; replicating H a user-definable number of times to form a two-dimensional array H; constructing a masking matrix W with a user-definable spatially-coupled pattern; and masking a sub-matrix of Husing W to obtain a spatially-coupled parity-check matrix H, wherein a null space of His the algebraic SC LDPC code.

Method of and apparatus for generating spatially-coupled low-density parity-check code

A communication system includes: a receiver resource module configured to determine a receiver-antenna count for characterizing a device; an adjustment module, coupled to the receiver resource module, configured to generate a receiver-capacity profile exceeding a capability associated with the receiver-antenna count with a control unit; and a report module, coupled to the adjustment module, configured to transmit the receiver-capacity profile for communicating a communication content for communicating with the device.

Communication system with communication-layer maximization mechanism and method of operation thereof

A computing system includes: an inter-device interface configured to access a destination signal including an information portion for representing a content and an error-handling portion for describing the information portion relative to the content; a communication unit, coupled to the inter-device interface, configured to: generate a parity-check parameter based on a sparse configuration from the destination signal, and estimate the content based on decoding the information portion using the error-handling portion and the parity-check parameter.

Computing system with error handling mechanism and method of operation thereof

A electronic system includes: a support chip configured to receive an input code stream; a circular Viterbi mechanism, coupled to the support chip, configured to: generate a final path metric for the input code stream, store intermediate path metrics at the repetition depth, generate a repetition path metric for the input code stream, and calculate a soft correlation metric based on the final path metric, the repetition path metric, and the intermediate path metrics.

Electronic system with Viterbi decoder mechanism and method of operation thereof

A communication system includes: a validation module configured to transmit a repeat request corresponding to a preceding data including a communication content; an inter-block processing module, coupled to the validation module, configured to determine a previous communication value based on the preceding data; a detection module, coupled to the inter-block processing module, configured to identify a repeat data corresponding to the repeat request from a receiver signal; an accumulator module, coupled to the detection module, configured to generate an accumulation output based on the preceding data and the repeat data; and a decoding module, coupled to the accumulator module, configured to determine the communication content using the previous communication value and the accumulation output across instances of transmission blocks for communicating with a device.

Communication system with repeat-response combining mechanism and method of operation thereof

A system, method and device for object identification is provided. The method of identifying objects includes, but is not limited to, calculating feature vectors of the object, calculating feature vectors of the object's context and surroundings, combining feature vectors of the object, calculating likelihood metrics of combined feature vectors, calculating verification likelihood metrics against contact list entries, calculating a joint verification likelihood metric using the verification likelihood metrics, and identifying the object based on the joint verification likelihood metric.

System and method for providing an on-chip context aware contact list

Growing community of inventors

San Diego, CA, United States of America

Inyup Kang