公开(公告)号：US20210377116A1

公开(公告)日：2021-12-02

申请号：US17404309

申请日：2021-08-17

申请人： Shu-ping Yeh ,  Jaemin Han ,  Wei Mao ,  Jingwen Bai ,  Murali Narasimha

发明人： Shu-ping Yeh ,  Jaemin Han ,  Wei Mao ,  Jingwen Bai ,  Murali Narasimha

IPC分类号： H04L12/24 ,  H04W24/10 ,  H04L12/26 ,  H04L1/00

摘要： Embodiments of a virtualized radio-access network (RAN) node configured to implement functions including an O-RAN Distributed Unit (DU), an O-RAN Radio Unit (RU), an O-RAN Central Unit-Control Plane (CU-CP), and an O-RAN Central Unit-User Plane (CU-UP), are described herein. In some embodiments, a reliability enhancement strategy is determined according to measurement reports collected from user equipment (UEs), the DU and the CU-UP.

公开(公告)号：US20230099849A1

公开(公告)日：2023-03-30

申请号：US17482949

申请日：2021-09-23

申请人： Jingwen Bai ,  Shu-ping Yeh ,  Yang-Seok Choi ,  Francesc Guim Bernat ,  Kshitij Arun Doshi ,  Ned M. Smith ,  Christian Maciocco

发明人： Jingwen Bai ,  Shu-ping Yeh ,  Yang-Seok Choi ,  Francesc Guim Bernat ,  Kshitij Arun Doshi ,  Ned M. Smith ,  Christian Maciocco

IPC分类号： H04W72/12 ,  H04W72/14 ,  H04W52/32

摘要： An apparatus for an S-BS includes processing circuitry configured to decode configuration signaling from a plurality of S-UEs. The configuration signaling indicates an interference level at each of the plurality of S-UEs from transmissions of an M-BS. An S-UE is selected based on a comparison of the interference level at each S-UE with an interference threshold of each S-UE. Control signaling is encoded for transmission to the selected S-UE via a small cell access (S-AC) communication link. The control signaling is transmitted during the reception of downlink data from the M-BS via a primary backhaul communication link.

公开(公告)号：US20220014963A1

公开(公告)日：2022-01-13

申请号：US17484743

申请日：2021-09-24

申请人： Shu-ping Yeh ,  Jingwen Bai ,  Shilpa Talwar

发明人： Shu-ping Yeh ,  Jingwen Bai ,  Shilpa Talwar

IPC分类号： H04W28/02 ,  G06N3/08

摘要： The present disclosure is related to multi-access traffic management in edge computing environments, and in particular, artificial intelligence (AI) and/or machine learning (ML) techniques for multi-access traffic management. A scalable AI/ML architecture for multi-access traffic management is provided. Reinforcement learning (RL) and/or Deep RL (DRL) approaches that learn policies and/or parameters for traffic management and/or for distributing multi-access traffic through interacting with the environment are also provided. Deep contextual bandit RL techniques for intelligent traffic management for edge networks are also provided. Other embodiments may be described and/or claimed.

公开(公告)号：US20220124560A1

公开(公告)日：2022-04-21

申请号：US17561948

申请日：2021-12-25

申请人： Shu-ping Yeh ,  Jingwen Bai

发明人： Shu-ping Yeh ,  Jingwen Bai

IPC分类号： H04W28/24 ,  H04W24/02 ,  H04W28/26 ,  H04W24/04 ,  H04W24/10 ,  H04W48/18

摘要： The present disclosure provides a resilient (radio) access network ((R)AN) slicing framework encompassing a resource planning engine and distributed dynamic slice-aware scheduling modules at one or more network access nodes, edge compute nodes, or cloud computing service. The resilient (R)AN slicing framework includes resource planning and slice-aware scheduling, as well as signaling exchanges for provisioning resilient (R)AN slicing. The intelligent (R)AN slicing framework can realize resource isolation in a more efficient and agile manner than existing network slicing technologies. Other embodiments may be described and/or claimed.

公开(公告)号：US20220124588A1

公开(公告)日：2022-04-21

申请号：US17561897

申请日：2021-12-24

申请人： Jing Zhu ,  Menglei Zhang ,  Juan Fang ,  Shu-ping Yeh ,  Shilpa Talwar ,  Valerie Parker

发明人： Jing Zhu ,  Menglei Zhang ,  Juan Fang ,  Shu-ping Yeh ,  Shilpa Talwar ,  Valerie Parker

IPC分类号： H04W36/22 ,  H04W36/00 ,  H04W28/02

摘要： The present disclosure is related to multi-queue management techniques and packet reordering techniques for inter-radio access technology (RAT) and intra-RAT traffic steering. The multi-queue management and packet reordering techniques may be used in Multi-Access Management Services (MAMS) framework, which is a programmable framework that provides mechanisms for the flexible selection of network paths in a multi-access (MX) communication environment, based on an application's needs. Other embodiments may be described and/or claimed.

公开(公告)号：US20220329522A1

公开(公告)日：2022-10-13

申请号：US17853331

申请日：2022-06-29

申请人： Christian Maciocco ,  Ren Wang ,  Kshitij Arun Doshi ,  Francesc Guim Bernat ,  Ned M. Smith ,  Satish Chandra Jha ,  Vesh Raj Sharma Banjade ,  S M Iftekharul Alam ,  Shu-ping Yeh

发明人： Christian Maciocco ,  Ren Wang ,  Kshitij Arun Doshi ,  Francesc Guim Bernat ,  Ned M. Smith ,  Satish Chandra Jha ,  Vesh Raj Sharma Banjade ,  S M Iftekharul Alam ,  Shu-ping Yeh

IPC分类号： H04L45/00 ,  H04L43/08 ,  H04L41/16

摘要： Disclosed are systems and methods for adaptive resilient network communication. A system may monitor network traffic on multiple pathways between user equipment and an application or a service at a network destination, gather network telemetry data from the monitored network traffic, input the network telemetry data into a trained artificial intelligence model, and classify the network telemetry data using the model. The system may further determine, using the model, an anomaly condition in at least a portion of the multiple pathways, and in response to the determination of an anomaly, select a mitigation technique for the at least a portion of the multiple pathways.

公开(公告)号：US20220038349A1

公开(公告)日：2022-02-03

申请号：US17504711

申请日：2021-10-19

申请人： Ziyi Li ,  Alexander Sirotkin ,  Youn Hyoung Heo ,  Shu-ping Yeh ,  Yi Guo

发明人： Ziyi Li ,  Alexander Sirotkin ,  Youn Hyoung Heo ,  Shu-ping Yeh ,  Yi Guo

IPC分类号： H04L12/24 ,  G06N20/00 ,  H04W24/02 ,  H04B17/391

摘要： An apparatus and system to provide a federated learning scheme between a RAN and connected UEs are described. A gNB-DU, gNB-CU, or LMF acts as a central server that selects an AI/ML model, trains the AI/ML model, and transmits the AI/ML model to UEs. The UEs act as local nodes that each send a model request to the central server, receive the AI/ML model in response to the request, trains the AI/ML model locally with data, and report updated parameters to the central server. The central server aggregates parameters from the local nodes and updates the AI/ML model.