Abstract: Embodiments of the disclosure provide a machine learning system for identifying and countering non-friendly radar networks. Methods of the disclosure include generating, in a machine learning module, an operational model of a radar network within an environment. An autonomous agent within the environment detects the radar network. The method also includes classifying the radar network as friendly or non-friendly based on the operational model. The method also includes generating, in a reinforcement learning module, a counter-radar maneuver based on the operational model in response to classifying the radar network as non-friendly. Embodiments of the disclosure implement the counter-radar maneuver via the autonomous agent in communication with the reinforcement learning module.

Abstract: A system for governing access to a network environment, including: at least one communication node communicatively coupled to a network infrastructure; a network assurance agent configured to monitor the at least one communication node, wherein the network assurance agent performs actions including: generating, in response to an access request for a network resource from the at least one communication node, an environmental model of the at least one communication node relative to the network environment, wherein the environmental model includes operational data of the at least one communication node or at least one other communication node in the network environment, calculating a risk score for the at least one communication node via a machine learning algorithm, based on the environmental model, and granting or denying the access request based on the risk score.

Abstract: The disclosure provides processes to generate an identification tag for a transponder in a wireless network via a machine learning module. Methods of the disclosure include capturing, via a wireless receiver within a wireless network, a plurality of time-domain communication samples of a transponder operating within the wireless network. A multi-dimensional tensor is generated, indicating a plurality of features of the time-domain communication samples of the transponder. A machine learning module converts the multi-dimensional tensor into a one-dimensional feature vector indicating the plurality of features of the time-domain communication samples. The machine learning module extracts a hardware identification feature for the transponder from the one-dimensional feature vector.

Abstract: A signal sensing and classification system, including: a detection module for obtaining a spectrum of signals; a separation module for extracting a signal from the spectrum of signals; and a multi-task learning (MTL) module for performing a plurality of tasks in parallel on the extracted signal using an MTL neural network model; the plurality of tasks including at least two of: determining a signal class of the extracted signal; determining a modulation class of the extracted signal; and determining at least one signal descriptor of the extracted signal. Additional tasks may include, for example, radio frequency (RF) fingerprinting on the extracted signal to identify an RF device that produced the extracted signal. Other classification and regression tasks may also be performed by the MTL neural network model.

Abstract: Embodiments of the disclosure provide a signal intelligence system for integrating radio frequency (RF) spectrum prediction with emitter classification and position, navigation, and timing (PNT). Methods of the disclosure include detecting a signal from an emitter operating within a wireless network. Continued processing includes forecasting, via a multi-task learning component of a machine learning module, one of a signal modulation, an emission protocol, and an identification of the emitter based on the detected signal. Additional processing includes calculating a positioning, navigation, and timing (PNT) profile for the emitter using a recurrent graph neural network (ReGNN) component of the machine learning module. Further processing includes predicting, via a recurrent neural network (RNN) of the machine learning module, a spectrum occupancy of the emitter within the wireless network.

Abstract: Embodiments of the disclosure provide distributed authentication with network segmentation and dynamic authorization for networks. The system may include a device within a network of devices. An identifier is within the device and includes a cryptographic certificate. The device is configured to transmit the identifier to an authenticator as a security proof. The authenticator is configured to disable the device from performing at least one operation within the network before verifying an identity of the device via the identifier.

Abstract: Embodiments of the disclosure provide a system and methodology for secure coexistence between wireless fidelity and cellular networks. Methods include: determining whether a wireless fidelity (WiFi) device is sharing a spectrum of a network; and adjusting a parameter of a cellular device sharing the spectrum in response to determining that the WiFi device is sharing the spectrum, wherein the parameter includes a modulation and coding scheme or a transmission power of the cellular device. Methods further include controlling admission of users to a communications network, including: determining whether an incoming transmission is an authentication request from a new user; issuing a temporary authentication to the user to transmit a set of access requirements to the user; determining whether a reply from the user complies with the set of access requirements; and issuing a long-term authentication to the user in response to the reply complying with the set of access requirements.

Abstract: Embodiments of the disclosure provide a system for operating a radio frequency (RF) network having a plurality of communication nodes. A network transceiver communicates with communication nodes in the RF network. A computing device coupled to the network transceiver performs actions including: evaluating a state of the RF network using a machine learning model, based on a spectrum environment and a communication objective, generating a set of communication parameters based on the state of the RF network, causing the network transceiver to communicate with the a communication node using the generated set of communication parameters, and modifying the machine learning model based on a result of causing the network transceiver to communicate with the communication node.

Abstract: A system for governing access to a network environment, including: at least one communication node communicatively coupled to a network infrastructure; a network assurance agent configured to monitor the at least one communication node, wherein the network assurance agent performs actions including: generating, in response to an access request for a network resource from the at least one communication node, an environmental model of the at least one communication node relative to the network environment, wherein the environmental model includes operational data of the at least one communication node or at least one other communication node in the network environment, calculating a risk score for the at least one communication node via a machine learning algorithm, based on the environmental model, and granting or denying the access request based on the risk score.

Abstract: Embodiments of the disclosure provide a transmission system, including: an encoder for encoding a set of information symbols into a set of encoded signals for transmission, wherein the encoder applies a full-diversity space-time block code (STBC) to the set of information symbols; and at least three antennas for transmitting the set of encoded signals over four epochs at a code rate of two.

Abstract: Embodiments of the disclosure provide a system for operating a radio frequency (RF) network having a plurality of communication nodes. A network transceiver communicates with communication nodes in the RF network. A computing device coupled to the network transceiver performs actions including: evaluating a state of the RF network using a machine learning model, based on a spectrum environment and a communication objective, generating a set of communication parameters based on the state of the RF network, causing the network transceiver to communicate with the a communication node using the generated set of communication parameters, and modifying the machine learning model based on a result of causing the network transceiver to communicate with the communication node.

Abstract: Embodiments of the disclosure provide a transmission system, including: an encoder for encoding a set of information symbols into a set of encoded signals for transmission, wherein the encoder applies a full-diversity space-time block code (STBC) to the set of information symbols; and at least three antennas for transmitting the set of encoded signals over four epochs at a code rate of two.

Abstract: An apparatus and method for providing a protocol stack design which resists active jammers. The apparatus including a transmitter component, the transmitter component including: a rate-2 orthogonal space-time block code (OSTBC) encoder for processing a set of information symbols to produce a set of encoded signals; a precoder module coupled to an output of the rate-2 OSTBC encoder for modifying a signal-to-jammer plus noise ratio (SJNR) of the set of encoded signals; and an eigen-beamformer module coupled to an output of the precoder module, and configured to generate a set of symbols for transmission via a set of eigenmodes of a channel covariance matrix for the transceiver.

Abstract: An apparatus and method for providing a protocol stack design which resists active jammers. The apparatus including a transmitter component, the transmitter component including: a rate-2 orthogonal space-time block code (OSTBC) encoder for processing a set of information symbols to produce a set of encoded signals; a precoder module coupled to an output of the rate-2 OSTBC encoder for modifying a signal-to-jammer plus noise ratio (SJNR) of the set of encoded signals; and an eigen-beamformer module coupled to an output of the precoder module, and configured to generate a set of symbols for transmission via a set of eigenmodes of a channel covariance matrix for the transceiver.

Abstract: A vertebral assist device for monitoring, supporting, stabilizing, and adjusting vertebrae. An embodiment of the vertebral assist device includes: a plurality of vertebral support sections, each of the plurality of vertebral support sections configured to support a respective vertebra of a patient; an actuating system for interconnecting each adjacent pair of the plurality of vertebral support sections, the actuating system dynamically controlling an alignment of the plurality of vertebral support sections; and a control system for actively monitoring the alignment of the plurality of vertebral support sections and for directing the actuating system to dynamically adjust the alignment of the plurality of vertebral support sections until an alignment goal is achieved.

Abstract: Embodiments of the present disclosure relate to routing data through a distributed communications network (DCN) including several communication devices. Each communication device may include or be in communication with a computing device which causes the communication device to perform actions including: calculate a utility score for a subset of the plurality of communication devices, the utility score quantifying transmissibility of a data packet from the transceiver assembly to another communication device. The utility score may be calculated based on: a distance between the transceiver assembly and the communication device, a transmission queue at the transceiver assembly, a remaining life of the power source, a power consumption to transmit the data packet to the communication device, and a data throughput of the communication device. The computing device may generate instructions to transmit the data packet to at least one communication device in the subset, based on the calculated utility score.

Abstract: A vertebral assist device for monitoring, supporting, stabilizing, and adjusting vertebrae. An embodiment of the vertebral assist device includes: a plurality of vertebral support sections, each of the plurality of vertebral support sections configured to support a respective vertebra of a patient; an actuating system for interconnecting each adjacent pair of the plurality of vertebral support sections, the actuating system dynamically controlling an alignment of the plurality of vertebral support sections; and a control system for actively monitoring the alignment of the plurality of vertebral support sections and for directing the actuating system to dynamically adjust the alignment of the plurality of vertebral support sections until an alignment goal is achieved.

Abstract: Embodiments of the present disclosure relate to routing data through a distributed communications network (DCN) including several communication devices. Each communication device may include or be in communication with a computing device which causes the communication device to perform actions including: calculate a utility score for a subset of the plurality of communication devices, the utility score quantifying transmissibility of a data packet from the transceiver assembly to another communication device. The utility score may be calculated based on: a distance between the transceiver assembly and the communication device, a transmission queue at the transceiver assembly, a remaining life of the power source, a power consumption to transmit the data packet to the communication device, and a data throughput of the communication device. The computing device may generate instructions to transmit the data packet to at least one communication device in the subset, based on the calculated utility score.