Northeastern University

The State University of New York

Bionet Sonar

A10 Systems LLC

Tommaso Melodia

Giuseppe Enrico Santagati

Francesco Restuccia

Salvatore D'Oro

Emrecan Demirors

Zhangyu Guan

Leonardo Bonati

Michele Polese

Lorenzo Bertizzolo

Jorge Jimenez

Laura Galluccio

Raffaele Guida

Sergio Palazzo

Junaid Islam

David Simpson

Bryan Crompton

Apurva N Mody

Dap Minh Tran

Luca Baldesi

Kerem Enhos

Daniel Uvaydov

Hai Cheng

Salvatore D'oro

Provided herein are open radio access network (Open RAN) adapters for using non-open RAN nodes in a wireless network having Open RAN infrastructure, the Open RAN adapters including one or more control application programming interfaces (Control APIs) for controlling the non-open RAN nodes, one or more Data Capture APIs for capturing operational data from the non-open RAN nodes, one or more Open RAN interfaces for communication between the Open RAN adapter and the Open RAN infrastructure and one or more software modules each configured to translate data received via the Data Capture APIs to Open RAN compatible data for use by the Open RAN infrastructure and/or translate Open RAN commands received via the Open RAN interfaces to non-open RAN compatible commands for instantiation at one or more of the non-open RAN nodes via the Control APIs.

Open RAN adapter for use of open RAN technologies on legacy RAN systems

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.

System and methods for using ultrasonic waves for wireless powering and communication of a cardiac assist device

Method and systems of sharing a frequency spectrum between a primary wireless network and a secondary wireless network coexisting with the primary wireless network are provided. At a node of the secondary network, at a bottom of a protocol stack integrated between a plurality of antennas and upper layers of the protocol stack, a beamforming scheme is determined for transmission to scheduled users of the secondary network. Using this technology, a newly-deployed wireless network can access a spectrum band based on cognitive beamforming without mutual temporal exclusion. i.e., without interrupting the ongoing transmissions of coexisting wireless networks on the same bands, and without cross-technology communication.

Method for spectrum sharing by primary and secondary networks based on cognitive beamforming

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.

System for frequency sharing in open radio access networks using artificial intelligence

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.

System and method for interference mitigation and congestion control through cross layer cognitive communications and intelligent routing

Provided herein are systems for controlling a network of distributed non-terrestrial nodes including a control framework operative to train and control a plurality of the non-terrestrial nodes, the control framework including a control interface in communication with a network operator to receive one or more specified control objectives, and a learning engine operative to train a virtual non-terrestrial network, wherein the control framework is further operative to transfer knowledge gained through the training of the virtual non-terrestrial network to the network of distributed non-terrestrial nodes as data-driven logic unit configurations tailored for the specified control objectives.

Distributed deep reinforcement learning framework for software-defined unmanned aerial vehicle network control

Methods and systems are provided for controlling an unmanned aerial vehicle (UAV) swarm network including receiving, at a control terminal, an operator input defining a plurality of network performance directives, constructing a network control model representing the plurality of network performance directives, decomposing the network control model into a plurality of sub-models, generating, via the control terminal, a distributed UAV control model corresponding to each of the distributed sub-models, transmitting the generated distributed UAV control models to each of a plurality of UAVs of the UAV swarm network, executing, at each of the plurality of UAVs, at least one of the distributed UAV control models, and changing, responsive to the execution of the distributed UAV control models, an operating condition of at least one of the plurality of UAVs to satisfy the network performance directives.

Software defined drone network control system

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 waveforms waveform characteristics of incoming RF transmissions beams, and an angle of arrival engine operative to determine an angle of arrival of the incoming RF transmissions beams on an antenna array. An incoming RF transmission beam and angle of arrival are selected based on the determined waveforms for beam management operations.

Coordination-free mmWave beam management with deep waveform learning

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.

Operating system for software-defined cellular networks

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.

Visible-light software-defined modem

A radio access network (RAN) intelligent controller (RIC) and corresponding method may be implemented within RAN and in next-generation cellular networks to improve performance. The RIC comprises an interface to a RAN and further comprises a data-driven logic unit. The data-driven logic unit (i) produces, based on data received from the RAN via the interface, a representation describing a state of the RAN and (ii) based on the representation describing the state, instructs an action associated with at least one network element. The interface transmits a message based on the action instructed. The message is to be routed to the at least one network element. The representation is based on a context of the RAN. The message transmitted enabling re-configuration of the at least one network element. The re-configuration improves performance of the at least one network element within the context.

Intelligence and learning in O-RAN for 5G and 6G cellular networks

Methods and systems are provided for allocating resources to users in a wireless network including a plurality of edge nodes that provide wireless network access and multi-access edge computing functions. Slice requests are received from the users for a type of resource, including one or more of networking resources, storage resources, and computation resources. A set of slice requests to be admitted is determined based on resource availability constraints among one or more of the networking resources, the storage resources, and the computation resources at each edge node.

Methods for multi-access edge computing network slicing in 5G networks

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.

Private 5G cellular connectivity as a service through full-stack wireless steganography

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. According to some embodiments, the gate-level digital hardware module may be configured to process a data unit in accordance with a first communications protocol by directing the data unit through a first sequence of logic blocks, and process a subsequent data unit in accordance with a second communications protocol by directing the subsequent data unit through a second of sequence logic blocks.

Method and apparatus for wireless communications

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.

Growing community of inventors

Newton, MA, United States of America

Tommaso Melodia