Intel Corporation

Vasuki Narasimha Swamy

Shilpa Talwar

Hosein Nikopour

Oner Orhan

Feng Xue

Venkatesan Nallampatti Ekambaram

Kerstin Johnsson

Karthik Narayanan

Vinayak Honkote

Marcel Nassar

Dibyendu Ghosh

Ganeshram Nandakumar

Alexander Pyattaev

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

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

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

Various methods and devices for positioning autonomous agents including verifying a reported agent location using physical attributes of the received signal; improving agent formation for iterative localization; selecting agents for distributed task sharing; intelligent beacon-placement for group localization; relative heading and orientation determination utilizing time of flight; and secure Instrument Landing System (ILS) implementation for unmanned agents.

Unmanned vehicle positioning, positioning-based methods and devices therefor

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San Francisco, CA, United States of America

Vasuki Narasimha Swamy