Intel Corporation

Apple Inc.

Tahoe Research, Ltd.

General Instrument Corporation

Intel IP Corporation

Nageen Himayat

Shilpa Talwar

Alexander Sasha Sirotkin

Jing Zhu

Shu-Ping Yeh

Mo-Han Fong

Srikathyayani Srikanteswara

Candy Yiu

Jerome Parron

Hyung-Nam Choi

Vivek G Gupta

Christian Maciocco

Hassnaa Moustafa

Dave A Cavalcanti

Maruti Gupta Hyde

Umesh Phuyal

Kerstin Johnsson

Ravikumar Balakrishnan

Francesc Guim Bernat

Kshitij Arun Doshi

Markus Dominik Mueck

Zhibin Yu

Thorsten Clevorn

Venkatesan Nallampatti Ekambaram

Ingolf Karls

Rath Vannithamby

Alexandre Saso Stojanovski

Feng Xue

S M Iftekharul Alam

Jaroslaw Sydir

Eve M Schooler

Qinghua Li

Ned M Smith

Richard Charles Burbidge

Satish Chandra Jha

Kamran Etemad

Wook Bong Lee

Harry G Skinner

Vesh Raj Sharma Banjade

Huaning Niu

Bernhard Raaf

Hosein Nikopour

Sangeetha L Bangolae

Bertram Gunzelmann

Vallabhajosyula S Somayazulu

Biljana Badic

Sumeet Sandhu

Alexander Bachmutsky

Sebastian Schoenberg

Jong-Kae Fwu

Thomas J Kenney

Muthaiah Venkatachalam

Shahrnaz Azizi

Konstantinos Dimou

Carlos Horacio Aldana

Marta Martinez Tarradell

Christian Drewes

John Joseph Browne

Robert Zaus

Simon R Hunt

Pingping Zong

Debabani Choudhury

Ofer Hareuveni

Duncan Kitchin

Gabriel Arrobo Vidal

Carlos Cordeiro

Debdeep Chatterjee

Van H Le

Emily H Qi

Ozgur Oyman

Bahareh Sadeghi

Yang-Seok Choi

RongZhen Yang

Linda L Hurd

Marcio Rogerio Juliato

Ana Lucia Pinheiro

Yuval Shamuel Lomnitz

Rafael Misoczki

Oner Orhan

Yuan Zhu

Ariela Zeira

Charles Anthony Eldering

Rita H Wouhaybi

Jeongho Jeon

Ren Wang

Ping Wang

Giuseppe Beppe Raffa

Ignacio Javier Alvarez

Min Suet Lim

Claudio Da Silva

Farid Adrangi

Shekoufeh Qawami

Liuyang L Yang

Sabine Roessel

Stefano Pellerano

Ana Lucia A Pinheiro

Sudeep Manithara Vamanan

Honggang Li

Vijay Sarathi Kesavan

Xiruo Liu

According to various examples, a communication system is described comprising a receiver configured to receive, for each of a plurality of object classes, via a wireless communication channel shared among transmitters of a respective set of transmitters, a superposition of transmitted hyperdimensional code words, comprising, for each transmitter of the respective set of transmitters, a hyperdimensional code word transmitted via the wireless communication channel and encoding data of an object of the object class acquired by the transmitter, a memory configured to store, for each of the plurality of object classes, the received superposition in association with the class, a processor configured to classify a hyperdimensional code word representing an object to be classified by correlating the hyperdimensional code word with each stored superposition and to generate a classification result corresponding to the object class associated with a superposition fulfilling a predetermined criterion based on correlation results.

Communication systems and methods for transmitting and processing of data

Methods, apparatus, systems, and articles of manufacture are disclosed to improve mobile edge platform resiliency. An example apparatus includes memory, instructions, and processor circuitry to execute the instructions to initiate a search for one of an access point or a cluster head at a first time, in response to detection of the cluster head, initiate an intra-cluster mobile edge platform (MEP) mutual authentication associated with the cluster head, cause transmission of historical activity data to at least one MEP associated with the cluster head, and cause storage of an authentication metric in a secure storage, the authentication metric based on historical connectivity data.

Methods, systems, articles of manufacture and apparatus to improve mobile edge platform resiliency

In one embodiment, a node of a data centric network (DCN) may receive a first service request interest packet from another node of the DCN, the first service request interest packet indicating a set of functions to be performed on source data to implement a service. The node may determine that it can perform a particular function of the set of functions, and determine, based on a backlog information corresponding to the particular function, whether to commit to performing the particular function or to forward the service request interest packet to another node. The node may make the determination further based on service delivery information indicating, for each face of the node, a service delivery distance for implementing the set of functions.

Queuing control for distributed compute network orchestration

An apparatus of a transmitter computing node n (TX node n) of a wireless network, one or more computer readable media, a system, and a method. The apparatus includes one or more processors to: implement machine learning (ML) based training rounds, each training round including: determining a local action value function Q(h, a; θ) corresponding to a value of performing a radio resource management (RRM) action aat a receiving computing node n (RX node n) associated with TX node n using policy parameter θand based on h, hincluding channel state information at RX node n; and determining, based on an overall action value function Qat time t, an estimated gradient of an overall loss at time t for overall policy parameter θ(∇L(θ)), wherein Qcorresponds to a mixing of local action value functions Q(h, a; θ) for all TX nodes i in the network at time t including TX node n; and determine, in response to a determination that ∇L(θ) is close to zero for various values of t during training, a trained local action value function Qto generate a trained action value relating to data communication between TX node n and RX node n.

Apparatus, system, method and computer-implemented storage media to implement radio resource management policies using machine learning

Methods, apparatus, systems, and articles of manufacture are disclosed for data resiliency in an edge network environment. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to at least one of execute and/or instantiate the instructions to generate spectrum metadata based on spectrum data, determine a resiliency operation based on one or more resiliency requirements, generate a resiliency policy based on at least one of the resiliency operation or the one or more resiliency requirements, generate a resiliency operation map based on at least one of the resiliency policy or first identifiers of respective workloads associated with the network environment, the first identifiers including a second identifier, and, in response to identifying a FAFO event associated with the second identifier, execute the resiliency operation based on mapping the second identifier to the resiliency operation in the resiliency operation map.

Systems, apparatus, and methods for data resiliency in an edge network environment

A digital twin model is generated for physical nodes of an edge network. The digital twin model includes a digital twin for a physical node of the physical nodes. An error is identified of the physical node or the digital twin for the physical node. The digital twin model is updated to halt communication with the physical node or the digital twin of the physical node. A path is created to another physical node or a digital twin of the another physical node in the digital twin model.

Digital twin framework for next generation networks

The present disclosure relates to a system for use in a wireless network, the system including: a processor configured to determine a performance parameter representative of a performance of a model of radio resource management, the model operating on a radio access network environment; and a radio resource manager configured to perform radio resource management dependent on the performance parameter.

Radio resource model management in a wireless network

System and techniques for information centric network (ICN) distributed path selection are described herein. An ICN node transmits a probes message to other ICN nodes. The ICN node receives a response to the probe message and derives a path strength metric from the response. Later, when a discovery packet is received by the ICN node, the path strength metric is added to the discovery packet.

Information centric network distributed path selection

Systems and techniques for cross-layer automated fault tracking and anomaly detection are described herein. Anomaly data may be obtained from a plurality of layers of a network. Elements of the anomaly data may be identified that correspond to a data flow of an application executing on the network. An artificial intelligence model may be trained using the elements of the anomaly data to generate an impact score for the application. The impact score may be generated for the application by evaluating current network metrics using the artificial intelligence model. An operational component of the network may be modified based on the impact score.

Cross-layer automated fault tracking and anomaly detection

System and techniques for geographic routing are described herein. A node receives a data packet that includes map data, a sequence of geographic areas to a requestor, and a target geographic area. The node may then determine that it is within the target geographic area and start a transmit timer based on a next-hop geographic area. Here, the next-hop geographic area is determined from the sequence of geographic areas in the data packet. The node may then counts how many other nodes from the geographic area sent data packets while the transmit timer is running. When the transmit timer expires, the node may transmit a modified data packet when the number of data packets is less than a predefined threshold. Here, the modified data packet is the data packet updated to include local map data and the next-hop geographic area.

Geographic routing

System and techniques for storage node recruitment in an information centric network (ICN) are described herein. An ICN node receives a storage interest packet that includes an indication differentiating the storage interest from other ICN interests. The ICN node forwards the storage interest packet and receives a storage data packet in response. Here, the storage data packet includes an indication that the storage data packet is not to be cached along with node information for a node that created the storage data packet. The ICN node may then transmit the storage data packet in accordance with a pending interest table (PIT) entry corresponding to the storage interest packet.

Storage node recruitment in an information centric network

Apparatuses, methods and storage medium associated with computer-assisted or autonomous driving (CA/AD) vehicles are disclosed herein. In embodiments, CA/AD vehicles are members of a CA/AD vehicle social network (CASN) in which various CA/AD vehicles may form connections or relationships with one another. CA/AD vehicles that have an existing relationship or connection may share CASN information with one another. The CASN information may include authenticated and/or proprietary information. Other embodiments are also described and claimed.

Computer-assisted or autonomous driving vehicles social network

An apparatus of an edge computing node, a method, and a machine-readable storage medium. The apparatus is to decode messages from a plurality of clients within the edge computing network, the messages including respective coded data for respective ones of the plurality of clients; computing estimates of metrics related to a global model for federated learning using the coded data, the metrics including a gradient on the coded data; use the metrics to update the global model to generate an updated global model, wherein the edge computing node is to update the global model by calculating the gradient on the coded data based on a linear fit of the global model to estimated labels from the federated learning; and send a message including the updated global model for transmission to at least some of the clients.

Uses of coded data at multi-access edge computing server

Embodiments of the present disclosure are directed towards devices and methods for identifying preferred access networks based at least in part on access network information including access network assistance information, steering policies, or access commands. In some embodiments, conflicts between access network information and access network discovery and selection function (ANDSF) policies may be rectified in identifying a preferred access network.

Access network discovery and selection

A method of implementing distributed AI or ML learning for autonomous vehicles is disclosed. An AI or ML model specific to a location or a type of the location is generated at a vehicle. In response to a detection that the vehicle is within a proximity to a road side unit (RSU) associated with the location or the type of the location or the vehicle is within a proximity to an additional vehicle that is present or anticipated to be present at the location or the type of the location, causing an AI or ML model transmission to the additional vehicle or the RSU. Based on the causing the AI or ML model reception, causing deployment of an additional AI or ML model in the vehicle to optimize the vehicle for the location or the type of the location.

Distributed learning to learn context-specific driving patterns

Growing community of inventors

Fremont, CA, United States of America

Nageen Himayat