Abstract: Methods for ultrasonic communications through biological tissue using ultrasonic pulses are disclosed. For example, methods for calculating a forward data generation rate and a forward transmission probability profile for ultrasonic communications through biological material are disclosed. The method may comprise measuring sets interference values corresponding to instants on a communication channel. First and second order moments may be calculated for each instant based on the measured interference values. An outage probability may be calculated for each set and the forward data generation rate and forward transmission probability profile may be calculated based on the outage probability value and other parameters and a threshold transmission rate, a transmission delay threshold, and a residual transmission error rate.

Abstract: A system and method for transmitting data ultrasonically through biological tissue employs a network of a plurality of nodes, at least a portion of the nodes implantable within the biological tissue. At least one implanted node includes a transmitter having an orthogonal frequency division multiplex signal generator to encode an ultrasonic signal for transmission through the biological tissue to an ultrasonic receiver at another node.

Abstract: A system and method is provided for transmitting signals ultrasonically among a network of implantable and wearable biological devices. The devices includes one or more implantable nodes, which include a sensing and/or actuating unit, at least one gateway node, and at least one access point node. Ultrasonic signals can be transmitted through the body by the implantable nodes to and from the gateway node, for transmission to and from the access point node. The access point node can be connected to the Internet. In this manner, remote instructions can be transmitted to the implantable nodes and data obtained at the implantable nodes can be transmitted to remote sites.

Abstract: A method of determining a response of a radio frequency wireless communication system to an adversarial attack is provided. Adversarial signals from an adversarial node are transmitted to confuse a target neural network of the communication system. An accuracy of classification of the incoming signals by the target neural network is determined.

Abstract: Methods and devices for transmitting and receiving data through biological tissue using ultrasonic pulses are described. Methods of the present invention may set an initial time-hopping frame length and an initial spreading code length for data transmission. A request-to-transmit may be sent from a transmitter over a control channel at the initial frame and code length. The receiver may respond to the transmitter with a clear-to-transmit packet having feedback information. The feedback information can be used to improve a forward time-hopping frame length and a forward spreading code length. Embodiments of the invention may involve a body area network or body surface network comprising a plurality of implanted sensor nodes operating according to the disclosed invention.

Abstract: Wirelessly networked systems of implantable and non-implantable medical devices with networking protocols, software, and hardware that allow for communications and energy transfer between different the medical devices (free standing, implants and wearables) using ultrasonic waves. The networks and methods of use are used to construct cardiac pacing, deep brain stimulation, and neurostimulation networks based on ultrasonic wide band technology.

Abstract: A method of determining a response of a radio frequency wireless communication system to an adversarial attack is provided. Adversarial signals from an adversarial node are transmitted to confuse a target neural network of the communication system. An accuracy of classification of the incoming signals by the target neural network is determined.

Abstract: A method of dynamically routing packets to a destination node performed by a computing device is disclosed. The method includes: (1) detecting a status of a plurality of links to the destination node across a plurality of communications modalities; (2) determining a set of links to use for routing packets to the destination node based on the detected statuses; and (3) sending packets to the destination node via the determined set of links. A related computer program product, apparatus, and system are also disclosed.

Abstract: 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.

Abstract: A machine learning (ML) agent operates at a transmitter to optimize signals transmitted across a communications channel. A physical signal modifier modifies a physical layer signal prior to transmission as a function of a set of signal modification parameters to produce a modified physical layer signal. The ML agent parses a feedback signal from a receiver across the communications channel, and determines a present tuning status as a function of the signal modification parameters and the feedback signal. The ML agent generates subsequent signal modification parameters based on the present tuning status and a set of stored tuning statuses, thereby updating the physical signal modifier to generate a subsequent modified physical layer signal to be transmitted across the communications channel.

Abstract: 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.

Abstract: A system and method for beam management in a wireless network are provided. A learning module having a trained classification module processes received I/Q input samples to determine transmitted beam information of incoming RF transmissions. The learning module includes a beam inference engine to determine waveform characteristics of incoming RF transmissions, and an angle of arrival engine operative to determine an angle of arrival of the incoming RF transmissions on an antenna array. An incoming RF transmission and angle of arrival are selected based on the determined waveforms for beam management operations.

Abstract: An underwater acoustic communication system for a mobile electronic device, such as a smartphone, has a communication unit with one or more ultrasonic transducers to transmit and receive underwater ultrasonic signals. The communication unit is connected to an audio auxiliary interface of the mobile electronic device. A processing unit in communication with the auxiliary interface receives RF signals from and transmits RF signals to the communication unit via the audio auxiliary interface.

Abstract: A visible light communication (VLC) system transmits and receives visible light signals across a VLC channel that includes an air-water interface. A transmitter may be configured generates and transmits a visible light signal across the VLC channel to a remote device, and a signal modulator controls the transmitter to generate the visible light signal from a digital transmission signal in accordance with a modulation setting. A receiver processes a remote visible light signal received across the VLC channel from the remote device. A signal demodulator converts the remote visible light signal to a received digital signal.

Abstract: The present invention describes a system that uses ultrasonic waves to transfer energy and data, enabling for the control and recharging of a cardiac assist device. Data and energy transfer are accomplished using pulsed ultrasonic waves. The use of ultrasonic waves allows for wireless transcutaneous energy transfer to power the cardiac assist device pump in absence of a driveline, reducing complications associated with driveline infections and improving patient quality of life.

Abstract: A networking device, such as an Internet of Things (IoT) device, implements an operative neural network (ONN) to optimize an internal wireless transceiver based on detected radio frequency (RF) spectrum conditions. The wireless transceiver detects the RF spectrum conditions local to the networking device and generates a representation of the RF spectrum conditions. The ONN determines transceiver parameters based on the RF spectrum conditions. A controller causes the representation of the RF spectrum conditions to be transmitted to a network node. Independent of the networking device, a training neural network (TNN) is trained based on the representation of the RF spectrum conditions, and neural network (NN) parameters are generated via the training a function of the representation of the RF spectrum conditions. The controller then reconfigures the ONN based on the NN parameters.

Abstract: Embodiments disclosed herein may be implemented in the form of a method or corresponding apparatus for receiving or transmitting network communications carried at acoustic wavelengths via an acoustic medium. The corresponding method or apparatus may include a gate-level digital hardware module communicatively coupled to a communications module and define therein logic blocks configured to perform respective primitive processing functions, sequences of the logic blocks being capable of processing data units in accordance with any of the multiple communications protocols on a data unit-by-data unit basis without reconfiguring.

Abstract: A machine learning (ML) agent operates at a transmitter to optimize signals transmitted across a communications channel. A physical signal modifier modifies a physical layer signal prior to transmission as a function of a set of signal modification parameters to produce a modified physical layer signal. The ML agent parses a feedback signal from a receiver across the communications channel, and determines a present tuning status as a function of the signal modification parameters and the feedback signal. The ML agent generates subsequent signal modification parameters based on the present tuning status and a set of stored tuning statuses, thereby updating the physical signal modifier to generate a subsequent modified physical layer signal to be transmitted across the communications channel.

Abstract: 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.

Abstract: A polymorphic platform for wireless communication systems is provided that employs trained classification techniques to determine physical layer parameters from a transmitter at a receiver. The system includes a learning module to determine transmitted physical layer parameters of the signal using a trained classification module, such as a deep learning neural network. The trained classification module receives I/Q input samples from receiver circuitry and processes the I/Q input samples to determine transmitted physical layer parameters from the transmitter. The system includes a polymorphic processing unit that demodulates data from the signal based on the determined transmitted parameters.