Northeastern University

Lorenzo Bertizzolo

Tommaso Melodia

Salvatore D'Oro

Zhangyu Guan

Hai Cheng

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

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.

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Boston, MA, United States of America

Lorenzo Bertizzolo