公开(公告)号：US11984034B2

公开(公告)日：2024-05-14

申请号：US16584978

申请日：2019-09-27

Applicant: Intel Corporation

Inventor： Dibyendu Ghosh ,  Vinayak Honkote ,  Kerstin Johnsson ,  Venkatesan Nallampatti Ekambaram ,  Ganeshram Nandakumar ,  Vasuki Narasimha Swamy ,  Karthik Narayanan ,  Alexander Pyattaev ,  Feng Xue

IPC: G05D1/10 ,  B64C39/02 ,  B64D47/08 ,  G05D1/00 ,  G08G5/00 ,  G08G5/02 ,  H04J3/14 ,  H04W4/42 ,  B64U10/13 ,  B64U101/20 ,  B64U101/30 ,  B64U101/60 ,  H04W84/00

CPC classification number: G08G5/0008 ,  B64C39/024 ,  B64D47/08 ,  G05D1/101 ,  G08G5/025 ,  H04J3/14 ,  H04W4/42 ,  B64U10/13 ,  B64U2101/20 ,  B64U2101/30 ,  B64U2101/60 ,  B64U2201/102 ,  B64U2201/104 ,  B64U2201/20 ,  H04W84/005

Abstract: 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.

公开(公告)号：US12245052B2

公开(公告)日：2025-03-04

申请号：US17483208

申请日：2021-09-23

Applicant: Intel Corporation

Inventor： Vasuki Narasimha Swamy ,  Hosein Nikopour ,  Oner Orhan ,  Shilpa Talwar

IPC: H04W24/02 ,  G06N3/045 ,  G06N3/08

Abstract: 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.

公开(公告)号：US20240023028A1

公开(公告)日：2024-01-18

申请号：US18470901

申请日：2023-09-20

Applicant: Intel Corporation

Inventor： Hosein Nikopour ,  Oner Orhan ,  Vasuki Narasimha Swamy

IPC: H04W52/22 ,  G06N3/08

CPC classification number: H04W52/223 ,  G06N3/08

Abstract: The present disclosure discusses network energy savings (NES) machine learning (ML) models that predict NES parameters used to adjust control parameters of respective network nodes in a wireless network, wherein the NES parameters can be used by the respective network nodes to adjust their control parameters, such that the wireless network realizes or achieves NES as a whole. The wireless network is represented as a graph with heterogeneous vertices that represent corresponding network nodes and edges that represent connections between the network nodes. The NES ML model comprises a graph neural network (GNN) and a fully connected neural network (FCNN). The GNN may be a graph convolutional neural network or a graph attention network. The FCNN may be a multi-layer perceptron, a deep neural network, and/or some other type of neural network. Other embodiments may be described and/or claimed.