Intel Corporation

Apple Inc.

Intel IP Corporation

Shu-Ping Yeh

Shilpa Talwar

Ned M Smith

Mo-Han Fong

Jingwen Bai

Jing Zhu

Menglei Zhang

Murali Narasimha

Hosein Nikopour

Yang-Seok Choi

Feng Xue

Francesc Guim Bernat

Youn Hyoung Heo

Umesh Phuyal

Juan Fang

Wei Mao

Kshitij Arun Doshi

Candy Yiu

Alexander Sasha Sirotkin

Yi Guo

Nageen Himayat

Christian Maciocco

Jerome Parron

Jaemin Han

Venkatesan Nallampatti Ekambaram

Ching-Yu Liao

David Arditti Ilitzky

Seong-Youp Suh

Sreenivas Kasturi

Ranganadh Karella

Eduardo Alban

Valerie J Parker

Mustafa Akdeniz

Junyoung Nam

Sasha Sirotkin

Tiefeng Shi

Sagar Dhakal

Ali Yazdan Panah

Jan Schreck

Mark Edward Davis

Timo Huusari

Ziyi Li

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

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

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.

Full duplex communications in wireless networks

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.

Resilient radio resource provisioning for network slicing

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

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

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

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.

E2 node and near real-time RAN intelligent controller (near-RT RIC) configured for PDCP duplication

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.

Federated learning across UE and RAN

Systems and methods of beamforming and improving mmWave communications for drones are described. Multiple RF chains are used to adapt the main beam to track changes without the use of pilot signals. To reduce interference, interfering signal power is eliminated by optimizing a non-Gaussian measure to extract the interferers. The AoA of signals from a target drone on neighbouring drones and location of the neighbouring drones and base stations are used to independently corroborate the location reported by the target drone. The base station provides additional synchronization signals below 6 GHz and restricts the search/measurement space in the vertical direction. The inherent sparse structure above 6 GHz is exploited by applying different beamformers on a sounding signal and estimating the AoA and impulse response. Variations of fully digital and hybrid beamforming architectures for multi-cell DL sync and CRS measurement are described.

Techniques for elevated device communication

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

Growing community of inventors

Campbell, CA, United States of America

Shu-Ping Yeh