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    Millimeter wave beam tracking and beam sweeping
    21.
    发明授权
    Millimeter wave beam tracking and beam sweeping 有权

    公开(公告)号：US11770174B2

    公开(公告)日：2023-09-26

    申请号：US17676379

    申请日：2022-02-21

    Applicant: Intel Corporation

    Inventor： Brent Elliott ,  Benjamin Grewell ,  Tom Harel ,  Junyoung Nam ,  Hosein Nikopour ,  Oner Orhan ,  Susruth Sudhakaran ,  Shilpa Talwar ,  Ping Wang ,  Liang Xian ,  Xiaodi Zhang

    IPC: H04B7/06 ,  H04B7/08

    CPC classification number: H04B7/0695 ,  H04B7/088

    Abstract: Aspects of mmWave beam tracking and beam sweeping are described, for example, spatial searching operations, directional beam forming, complex channel measurement operations, and adaptive power savings. Some aspects include using priori information for mmWave beam tracking and beam sweeping. Some aspects include using priori information to modify a superset of beam criteria to obtain a subset of beam criteria, select a spatial region according to the subset of beam criteria, and initiate a spatial searching operation within the spatial region for establishing a communication link. Some aspects include obtaining complex channel measurements of beams and combining the measurements with priori information to determine a beam for use in a communication link. Some aspects include providing signals from Nr over K1 input/output (IO) links and receiving signals over K1 IO links, and combining signals received over the K1 IO links, using a compression matrix, to generate signals over K IO links.

    METHODS AND APPARATUS TO DETERMINE TOPOLOGIES FOR NETWORKS
    22.
    发明申请
    METHODS AND APPARATUS TO DETERMINE TOPOLOGIES FOR NETWORKS 有权

    公开(公告)号：US20210119882A1

    公开(公告)日：2021-04-22

    申请号：US17133513

    申请日：2020-12-23

    Applicant: Intel Corporation

    Inventor： Oner Orhan ,  Shilpa Talwar ,  Marcel Nassar ,  Hosein Nikopour ,  Meryem Simsek ,  Oguz Elibol

    IPC: H04L12/24 ,  H04L5/00 ,  G06N20/00

    Abstract: Methods, apparatus, systems and articles of manufacture to determine topologies for networks are disclosed. An example a non-transitory computer readable medium comprises instructions that, when executed, cause a machine to at least: determine link capacities for a plurality of links between nodes of a network, determine a maximum number of children of the peer linked nodes, determine a maximum number of parents of the peer linked nodes, and utilize reinforcement learning to determine a subset of the plurality of links to be activated in the network based on the link capacities, the maximum number of children, and the maximum number of parents.

    LOW POWER mmWAVE RECEIVER ARCHITECTURE WITH SPATIAL COMPRESSION INTERFACE
    23.
    发明申请
    LOW POWER mmWAVE RECEIVER ARCHITECTURE WITH SPATIAL COMPRESSION INTERFACE 有权

    公开(公告)号：US20210075456A1

    公开(公告)日：2021-03-11

    申请号：US16958813

    申请日：2018-01-02

    Applicant: INTEL CORPORATION

    Inventor： Oner Orhan ,  Hosein Nikopour ,  Peter Sagazio ,  Farhana Sheikh ,  Junyoung Nam ,  Shilpa Talwar

    IPC: H04B1/16

    Abstract: 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.

    Multi-finger beamforming and array pattern synthesis
    26.
    发明授权
    Multi-finger beamforming and array pattern synthesis 有权

    公开(公告)号：US10334454B2

    公开(公告)日：2019-06-25

    申请号：US15592223

    申请日：2017-05-11

    Applicant: Intel Corporation

    Inventor： Oner Orhan ,  Eren Sasoglu ,  Hosein Nikopour ,  Shilpa Talwar

    IPC: H04W16/28 ,  H04W24/02 ,  H04B17/327 ,  H04W52/36 ,  H04W52/42 ,  H04B17/10 ,  H04B7/06

    Abstract: A communication device includes an antenna array, and a beamforming controller configured to determine a set of beamforming weights for the antenna array based on a target radiation pattern having a plurality of main fingers, wherein the beamforming controller is configured to, in each of a plurality of iterations identify a search space of beamforming weights for a plurality of elements of the antenna array, and determine, based on contribution of one or more of the plurality of elements of to multiple of the plurality of main fingers, an updated set of beamforming weights in the search space to reduce a difference between an actual radiation pattern and the target radiation pattern, the antenna array configured to transmit or receive radio signals based on the updated set of beamforming weights.

    Reinforcement learning (RL) and graph neural network (GNN)-based resource management for wireless access networks
    27.
    发明授权
    Reinforcement learning (RL) and graph neural network (GNN)-based resource management for wireless access networks 有权

    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.

    Wireless chip to chip communication with selective frequency multiplexing with different modulation schemes
    28.
    发明授权
    Wireless chip to chip communication with selective frequency multiplexing with different modulation schemes 有权

    公开(公告)号：US12088329B2

    公开(公告)日：2024-09-10

    申请号：US17131866

    申请日：2020-12-23

    Applicant: Intel Corporation

    Inventor： Mehnaz Rahman ,  Oner Orhan ,  Hosein Nikopour

    IPC: H04B1/00 ,  H04B1/04

    CPC classification number: H04B1/0057 ,  H04B1/0067 ,  H04B1/0483 ,  H04B2001/0491

    Abstract: A transmitter for chip to chip communication may include a modulator and a transmit frequency converter. The modulator may modulate a first received signal according to a first modulation scheme. The modulator may also modulate a second received signal according to a second modulation scheme. The transmit frequency converter may center the first received signal on a first frequency that does not comprise a phase within a radio frequency (RF) domain to generate a first centered signal. The transmit frequency converter may also center the second received signal on a second frequency that comprises a phase within the frequency band to generate a second centered signal. The second centered signal may be orthogonal to the first centered signal. A frequency gap may be positioned between the first centered signal and the second centered signal within the frequency band.

    Beam management and antenna calibration in MIMO systems
    29.
    发明授权
    Beam management and antenna calibration in MIMO systems 有权

    公开(公告)号：US12009875B2

    公开(公告)日：2024-06-11

    申请号：US17761940

    申请日：2019-12-27

    Applicant: Intel Corporation

    Inventor： Oner Orhan ,  Hosein Nikopour ,  Shilpa Talwar ,  Dor Shaviv ,  Roya Doostnejad ,  Adesoji J. Sajuyigbe

    IPC: H04B17/21 ,  H04B7/06

    CPC classification number: H04B17/21 ,  H04B7/0639 ,  H04B7/0695

    Abstract: 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.

    WIRELESS NETWORK ENERGY SAVING WITH GRAPH NEURAL NETWORKS
    30.
    发明公开
    WIRELESS NETWORK ENERGY SAVING WITH GRAPH NEURAL NETWORKS 审中-公开

    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.

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