Intel Corporation

Apple Inc.

Iospan Wireless, Inc.

Stanford Telecommunications, Inc.

Intel IP Corporation

Shilpa Talwar

Nageen Himayat

Hosein Nikopour

Shu-Ping Yeh

Oner Orhan

Yang-Seok Choi

Feng Xue

Kerstin Johnsson

Alexander Sasha Sirotkin

Debabani Choudhury

Mo-Han Fong

Vivek G Gupta

Jing Zhu

Ping Wang

Venkatesan Nallampatti Ekambaram

Qinghua Li

Eren Sasoglu

Jingwen Bai

Ehsan Aryafar

Junyoung Nam

Markus Dominik Mueck

Zhibin Yu

Dave A Cavalcanti

Ingolf Karls

Duncan Kitchin

Arnaud Lucres Amadjikpe

Roya Doostnejad

Yair Yona

Kamran Etemad

Thorsten Clevorn

Stefano Pellerano

Susruth Sudhakaran

Peter Sagazio

Liang Xian

Benjamin Grewell

Muthaiah Venkatachalam

Konstantinos Dimou

Srikathyayani Srikanteswara

Christian Drewes

Hassnaa Moustafa

Claudio Da Silva

Vallabhajosyula S Somayazulu

Minnie Ho

Jaroslaw Sydir

Brent Elliott

Navid Naderializadeh

Sagar Dhakal

Namyoon Lee

Huaning Niu

Jong-Kae Fwu

Thomas J Kenney

Xintian Eddie Lin

Bernhard Raaf

Hyung-Nam Choi

Shahrnaz Azizi

Alexandre Saso Stojanovski

Carlos Horacio Aldana

Kameran Azadet

Kathiravetpillai Sivanesan

Bryan K Casper

Marta Martinez Tarradell

Christian Maciocco

Ofir Degani

Bahareh Sadeghi

Wook Bong Lee

Arogyaswami J Paulraj

Ashoke Ravi

Michael Genossar

John Joseph Browne

Robert Zaus

Bertram Gunzelmann

Biljana Badic

Simon R Hunt

Harry G Skinner

Pingping Zong

Rotem Banin

Maruti Gupta Hyde

Sidharth Dalmia

Albert Molina

Kuilin Clark Chen

Georgios Yorgos Palaskas

Igal Kushnir

Saku Lahti

Yiting Liao

Jeffrey R Foerster

Juergen H Kreuchauf

Emanuel Cohen

Mikko S Komulainen

Gadi Shor

Jayprakash Thakur

Mikhail T Galeev

Stefan Franz

Kunal Datta

Moreno Ambrosin

Ralph Hasholzner

Menashe Soffer

Ana M Yepes

Moshe Teplitsky

Omer Asaf

Methods, apparatus, systems and articles of manufacture are disclosed to validate data communicated by a vehicle. An example apparatus an anomaly detector to, in response to data communicated by a vehicle, at least one of compare an estimated speed with a reported speed or compare a location of the vehicle with a reported location. The apparatus including the anomaly detector further to generate an indication of the vehicle in response to the comparison. The apparatus further includes a notifier to discard data sent by the vehicle and notify surrounding vehicles of the data communicated by the vehicle.

Methods and apparatus to validate data communicated by a vehicle

A system for wireless communication, the system may include a processor configured to generate encoded packets representative of input packets. The processor may also be configured to determine a probability of a transmission error for each link of a plurality of links. In addition, the processor may be configured to determine a probability of a link blockage error for each link of the plurality of links. Further, the processor may be configured to determine an allocation scheme for the encoded packets on the plurality of links based on the probability of the transmission error and the probability of the link blockage error for each link of the plurality of links. The processor may be configured to allocate the encoded packets on the plurality of links according to the allocation scheme. The processor may also be configured to instruct to wirelessly transmit the encoded packets on the plurality of links.

Packet encoding and allocation in multi-link systems

The present disclosure relates to a device for use in a wireless network, the device including: a processor configured to: provide input data to a trained graph neural network model, the input data being indicative of a graph representation of a plurality of wireless communication devices, wherein the trained graph neural network model is configured to provide output data being indicative of a scheduled user set including the plurality of wireless communication devices; and instruct user scheduling of the plurality of wireless communication devices based on the output data of the trained graph neural network model.

User scheduling using a graph neural network

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

Disclosed embodiments generally relate to edge-based multi-Radio Access Technology (RAT) traffic management (TM) solutions to support delay-sensitive traffic over heterogeneous networks. Embodiments include delay-aware TM implementations that split and/or steer network traffic across different RATs for the edge network control plane. Embodiments also include utilization threshold-based implementations to achieve delay-aware multi-path TM at the network's edge. The multi-path TM includes multi-RAT, multi-access, or multi-connectivity traffic routes. Embodiments include strategies to sort users (or devices) for making multi-RAT traffic distribution decisions and to determinate the utilization thresholds. Embodiments also include message exchange mechanisms or learning utilization thresholds and other useful system properties. Other embodiments may be described and/or claimed.

Multi-radio access technology traffic management

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.

Traffic steering and cross-layer and cross-link mobility management techniques for multi-access management services

The present disclosure provides connection management techniques based on graph neural networks (GNN) and deep reinforcement learning (DRL) to optimize user association and load balancing. A graph structure of a communication network is considered for the GNN architecture and DRL is used to learn parameters of the GNN algorithm/model. Connection management is defined as a combinatorial graph optimization problem, and the DRL mechanism uses the underlying graph to learn weights of the GNN for an optimal user connections or associations. The connection management techniques can consider local network features to make better decisions to balance network traffic load while network throughput is also maximized. Implementations are provided based on edge computing frameworks include the Open RAN (O-RAN) architecture. Other embodiments may be described and/or claimed.

Graph neural network and reinforcement learning techniques for connection management

The present disclosure is related to multi-access traffic management in multi-access computing environments. A reliability enhancement engine (REE) operated by a multi-access multipath traffic manager identifies traffic flows that have high reliability requirements ('reliability flows') and performs admission control for the reliability flows. The REE determines a coding rate (e.g., a redundancy factor) and traffic distribution strategies for the identified reliability flows. The REE applies cross-access network coding (NC) on the reliability flows, and handles multi-access acknowledgements and traffic pacing, which may involve multiplexing traffic of the reliability flows with non-high-reliability traffic. Other embodiments may be described and/or claimed.

Reliability enhancements for multi-access traffic management

Millimeter-wave (mmWave) and sub-mmWave technology, apparatuses, and methods that relate to transceivers and receivers for wireless communications are described. The various aspects include an apparatus of a communication device including an antenna array and processing circuitry coupled to the antenna array. The processing circuitry is configured to initialize a beam tracking algorithm based on received signals received at the antenna array, wherein antenna phases used in the beam tracking are bound by an upper phase limit and a lower phase limit, to generate a beam tracking result. The processing circuitry is further configured to generate a calibration vector based on the beam tracking result and receive subsequent transmissions using a codebook adapted based on the calibration vector.

Beam management and antenna calibration in MIMO systems

A computing node to implement an RL management entity in an NG wireless network includes a NIC and processing circuitry coupled to the NIC. The processing circuitry is configured to generate a plurality of network measurements for a corresponding plurality of network functions. The functions are configured as a plurality of ML models forming a multi-level hierarchy. Control signaling from an ML model of the plurality is decoded, the ML model being at a predetermined level (e.g., a lowest level) in the hierarchy. The control signaling is responsive to a corresponding network measurement and at least second control signaling from a second ML model at a level that is higher than the predetermined level. A plurality of reward functions is generated for training the ML models, based on the control signaling from the MLO model at the predetermined level in the multi-level hierarchy.

Reinforcement learning (RL) and graph neural network (GNN)-based resource management for wireless access networks

Millimeter wave (mmWave) technology, apparatuses, and methods that relate to transceivers, receivers, and antenna structures for wireless communications are described. The various aspects include co-located millimeter wave (mmWave) and near-field communication (NFC) antennas, scalable phased array radio transceiver architecture (SPARTA), phased array distributed communication system with MIMO support and phase noise synchronization over a single coax cable, communicating RF signals over cable (RFoC) in a distributed phased array communication system, clock noise leakage reduction, IF-to-RF companion chip for backwards and forwards compatibility and modularity, on-package matching networks, 5G scalable receiver (Rx) architecture, among others.

Wireless communication technology, apparatuses, and methods

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.

Reinforcement learning for multi-access traffic management

A receiver circuit associated with a communication device is disclosed. The receiver circuit comprises a digital data compression circuit configured to receive a plurality of digital receive signals derived from a plurality of analog receive signals respectively associated with the receiver circuit. The digital data compression circuit is further configured to compress the plurality of digital receive signals to form one or more compressed digital data signals based thereon, to be provided to an input output (I/O) interface associated therewith. In some embodiments, a compressed digital signal dimension associated with the one or more compressed digital data signals is less than a digital signal dimension associated with the plurality of digital receive signals.

Low power mmWave receiver architecture with spatial compression interface

The present disclosure is related to Multi-Access Management Services (MAMS), 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. Generic Multi-Access (GMA) functions are also integrated into the MAMS framework. The present disclosure discusses Per-Packet Prioritization (PPP), intra-flow classification, and Active Queue Management (AQM) techniques for MAMS/GMA systems. Other embodiments may be described and/or claimed.

Multi-access management service packet classification and prioritization techniques

Growing community of inventors

Los Altos, CA, United States of America

Shilpa Talwar