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23 条记录 (耗时 0.023 秒)

    Private 5G Cellular Connectivity as a Service Through Full-Stack Wireless Steganography
    2.
    发明申请
    Private 5G Cellular Connectivity as a Service Through Full-Stack Wireless Steganography 有权

    公开(公告)号：US20210352053A1

    公开(公告)日：2021-11-11

    申请号：US17316773

    申请日：2021-05-11

    申请人： Northeastern University

    发明人： Tommaso MELODIA ,  Leonardo BONATI ,  Salvatore D'ORO ,  Francesco RESTUCCIA

    IPC分类号： H04L29/06 ,  H04L5/00

    摘要： A steganographic communication system and method are provided. A covert packet generator can embed a stream of covert data as covert data symbols within primary I/Q symbols of a primary data stream in a covert packet. The covert packet has a data structure having a header, a payload, and a payload error detecting code. The header includes information on how to demodulate the covert packet by a receiver. The covert packet generator can also determine if a number of primary I/Q symbols is large enough to generate the header and can generate displacements in the primary I/Q symbols in a constellation diagram randomly in a plurality of transmissions to mimic channel noise. A transmitter and receiver can provide mutual authentication for covert transmissions.

    Embedded Networked Deep Learning for Implanted Medical Devices
    3.
    发明申请
    Embedded Networked Deep Learning for Implanted Medical Devices 有权

    公开(公告)号：US20210259639A1

    公开(公告)日：2021-08-26

    申请号：US17176229

    申请日：2021-02-16

    申请人： Northeastern University

    发明人： Daniel UVAYDOV ,  Raffaele GUIDA ,  Francesco RESTUCCIA ,  Tommaso MELODIA

    IPC分类号： A61B5/00 ,  G16H40/60 ,  G06N3/08 ,  G06N3/04

    摘要： A deep learning medical device implantable in a body is provided. The device includes a processing and communication unit and a sensing and actuation unit. The processing and communication unit includes a deep learning module including a neural network trained to process the input samples, received from the sensing and actuation unit, through a plurality of layers to classify physiological parameters and provide classification results. A communication interface in communication with the deep learning module receives the classification results for ultrasonic transmission through biological tissue. Methods of sensing and classifying physiological parameters of a body and methods of embedding deep learning into an implantable medical device are also provided.

    Real-Time Cognitive Wireless Networking Through Deep Learning in Transmission and Reception Communication Paths
    5.
    发明申请
    Real-Time Cognitive Wireless Networking Through Deep Learning in Transmission and Reception Communication Paths 有权

    公开(公告)号：US20210357742A1

    公开(公告)日：2021-11-18

    申请号：US16591772

    申请日：2019-10-03

    申请人： Northeastern University

    发明人： Francesco RESTUCCIA ,  Tommaso MELODIA

    IPC分类号： G06N3/08 ,  H04W24/02 ,  G06N3/04 ,  G06N3/063

    摘要： Apparatuses and methods for real-time spectrum-driven embedded wireless networking through deep learning are provided. Radio frequency, optical, or acoustic communication apparatus include a programmable logic system having a front-end configuration core, a learning core, and a learning actuation core. The learning core includes a deep learning neural network that receives and processes input in-phase/quadrature (I/Q) input samples through the neural network layers to extract RF, optical, or acoustic spectrum information. A processing system having a learning controller module controls operations of the learning core and the learning actuation core. The processing system and the programmable logic system are operable to configure one or more communication and networking parameters for transmission via the transceiver in response to extracted spectrum information.

    System for Frequency Sharing in Open Radio Access Networks Using Artificial Intelligence
    8.
    发明申请
    System for Frequency Sharing in Open Radio Access Networks Using Artificial Intelligence 有权

    公开(公告)号：US20230079529A1

    公开(公告)日：2023-03-16

    申请号：US17944687

    申请日：2022-09-14

    申请人： Northeastern University

    发明人： Luca BALDESI ,  Francesco RESTUCCIA ,  Tommaso MELODIA

    IPC分类号： H04W16/14 ,  H04W24/02 ,  H04W72/04

    摘要： Methods and systems are provided for frequency sharing in RANs using artificial intelligence including scanning, by a spectrum classification unit (SCU) of a channel-aware reactive mechanism (ChARM) app, a plurality of frequencies associated with ongoing communication, classifying, by a DNN of the SCU, I/Q samples of each of the scanned frequencies, the DNN executable via the one or more of the near-RT RIC, the DU, the RU, or combinations thereof, receiving, at a policy decision unit (PDU) from the SCU, the classified frequencies, applying, by the PDU, an embedded policy to the classified frequencies, transmitting commands from the PDU to a DU for making changes to the ongoing communication according to the applied policy, receiving, at a control interface implemented in the DU, the commands transmitted by the PDU, and changing, by the DU according to the commands, an operating parameter of a RU.

    Operating System for Software-Defined Cellular Networks
    9.
    发明申请
    Operating System for Software-Defined Cellular Networks 有权

    公开(公告)号：US20220248238A1

    公开(公告)日：2022-08-04

    申请号：US17605266

    申请日：2020-05-01

    申请人： Northeastern University

    发明人： Tommaso MELODIA ,  Leonardo BONATI ,  Salvatore D'ORO

    IPC分类号： H04W24/02

    摘要： A self-optimizing operating system for next-generation cellular networks is provided. The system provides telecommunications operators with an efficient and flexible network control platform that hides low-level network details through a virtual network abstraction and allows them to define centralized and high-level control objectives with no need for in-depth and detailed knowledge of cross-layer optimization theory or the underlying wireless protocol stack implementation.