Intel Corporation

Apple Inc.

Mustafa Akdeniz

Nageen Himayat

Candy Yiu

Ravikumar Balakrishnan

Jing Zhu

Thorsten Clevorn

Dave A Cavalcanti

Harry G Skinner

Feng Xue

Jeffrey R Foerster

Neelam Chandwani

Udayan Mukherjee

Brent Elliott

Chetan Hiremath

Mikhail T Galeev

Shadi Iskander

Susruth Sudhakaran

Jesus Martinez

Benjamin Grewell

Arjun Anand

Sagar Dhakal

Shilpa Talwar

Hosein Nikopour

Wook Bong Lee

Shu-Ping Yeh

Maruti Gupta Hyde

Ching-Yu Liao

Oner Orhan

David Arditti Ilitzky

Seong-Youp Suh

Jingwen Bai

Sreenivas Kasturi

Marcin Spoczynski

Ehsan Aryafar

Eduardo Alban

Ranganadh Karella

Navid Naderializadeh

Tiefeng Shi

Sergey Andreev

Mikhail Gerasimenko

Mark Edward Davis

Yevgeni Koucheryavy

Timo Huusari

Mark R Eisen

Jan Schreck

Bradley Alan Jackson

Daniel Tong

Philippe Auzas

Jonathan Byrne

Mengkun Ke

Rodrigo Aldana Lopez

David Gomez Gutierrez

Rafael De La Guardia Gonzalez

Leobardo Campos Macias

Nikita Tafintsev

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

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

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

Apparatuses of a user equipment (UE), a cellular base station, and radio access network (RAN) nodes are disclosed. An apparatus of a wireless communication device includes circuitry configured to measure reference signals received from a plurality of antennas of an other wireless communication device, and circuitry configured to cause one or more antennas of the wireless communication device to transmit information regarding the received reference signals back to the other wireless communication device to enable the other wireless communication device to estimate a utility function for different transmit parameter sets.

Management of MIMO communication systems

An unmanned aerial vehicle can be configured to adjust a beam direction, provide path information, act as a base station, act as a cluster head, include an improved directional antenna or array of directional antennas, communicate in a collaboration using belief propagation, receive communications from a serving station aiding in navigation or improved signal performance, or the like.

Unmanned aerial vehicle communication

An apparatus of a wireless device, such as a user equipment (UE), can include processing circuitry configured to perform one or more of the handover-related techniques disclosed herein. For example, when associated with moving with a plurality of mobile devices from coverage of a first cell to a second cell, the processing circuitry can detect the second cell. One or more parameters of the second cell can be measured. The one or more parameters can be communicated to one or more other mobile devices of the plurality of mobile devices.

Handover-related technology, apparatuses, and methods

Embodiments described herein are generally directed to a sequence encoding engine to apply pseudo-random label assignments to packets within a single transmission burst. They may also include a modulation circuit coupled to the sequence encoding engine to modulate signals to burst transmit the packets. These burst transmissions may travel along a plurality of radio access pathways. In embodiments, the pseudo-random label assignments to the packets within the single transmission burst using a van der Corput sequence, or some other sequence for example a digit-reversed base-N representation of a packet sequence number, wherein N is a number of the selected ones of the plurality of radio access pathways, for example but not limited to a LTE or a Wi-Fi pathway.

Pseudo-random label assignments for packets in a transmission burst

Methods, apparatus, systems and articles of manufacture are disclosed provide an apparatus comprising a data manager to transmit a first signal to a discovery drone, the first signal to cause the discovery drone to discover user equipment in an environment, the user equipment outside of a first network including a first drone; and collect one or more values corresponding to the user equipment; a utility function determiner to, in response to a trigger event corresponding to the one or more values, determine a first position of the first drone in the environment; and a navigation dispatcher to transmit a second signal to the first drone, the second signal to cause the first drone to regulate the first position to establish one or more connections between the user equipment and the first network.

Methods and apparatus to collect data from user equipment outside a network

Growing community of inventors

San Jose, CA, United States of America

Mustafa Akdeniz