公开(公告)号：US11784760B2

公开(公告)日：2023-10-10

申请号：US17108555

申请日：2020-12-01

Applicant: Samsung Electronics Co., Ltd.

Inventor： Mostafa El-Khamy ,  Arvind Yedla ,  Sang-Hyuck Ha ,  Hyunsang Cho ,  Inyup Kang

IPC: H04L5/00 ,  H04L1/1829 ,  H04L1/1822

CPC classification number: H04L1/1835 ,  H04L1/1822 ,  H04L5/001 ,  H04L5/0055

Abstract: 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.

公开(公告)号：US10862630B2

公开(公告)日：2020-12-08

申请号：US14847320

申请日：2015-09-08

Applicant: Samsung Electronics Co., Ltd.

Inventor： Mostafa El-Khamy ,  Arvind Yedla ,  SangHyuck Ha ,  Hyunsang Cho ,  Inyup Kang

IPC: H04L1/18 ,  H04L5/00

Abstract: 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.

公开(公告)号：US11189358B2

公开(公告)日：2021-11-30

申请号：US16919187

申请日：2020-07-02

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Seonghyeog Choi ,  Hongrak Son ,  Taehyun Song ,  Hyunsang Cho

IPC: G11C29/42 ,  G11C29/44 ,  G11C29/50 ,  G06N20/20 ,  G06N5/04 ,  G06K9/62

Abstract: According to a method of controlling an operation of a nonvolatile memory device using machine learning, operating conditions of the nonvolatile memory device are determined by performing an inferring operation using a machine learning model. Training data that are generated based on feature information and error information are collected, where the error information indicate results of error correction code (ECC) decoding of the nonvolatile memory device. The machine learning model is updated by performing a learning operation based on the training data. Optimized operating conditions for individual user environments are provided by collecting training data in the storage system and performing the learning operation and the inferring operation based on the training data.