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CROWN: Channel Responsive Operational Waveform for Airborne Networks

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-21-P-1334
Agency Tracking Number: FX21A-TCSO1-0318
Amount: $50,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF21A-TCSO1
Solicitation Number: X21.A
Solicitation Year: 2021
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-04-15
Award End Date (Contract End Date): 2021-07-19
Small Business Information
15400 Calhoun Drive Suite 190
Rockville, MD 20855-2814
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Yi Shi
 (301) 294-4628
Business Contact
 Mark James
Phone: (301) 294-5221
Research Institution
 University at Albany
 Stefan Brooks
1400 Washington Ave
Albany, NY 12222-0100
United States

 (518) 437-4557
 Nonprofit College or University

Current modular approaches to generate a waveform has led to efficient, versatile, and controllable communication systems that we have today with individually optimized processing blocks. However, this individual optimization process does not necessarily optimize the overall end-to-end communication system. Moreover, these designs are either channel agnostic or rely on accurate channel models and precise channel distribution assumptions. As a result, they do not meet the performance expectations for airborne, i.e., UAV, communications with very dynamic and complex channel environment and hardware impairments, and fall short of realizing the potential benefits of airborne communications. Intelligent Automation, Inc. (IAI) proposes to develop the Channel Responsive Operational Waveform for Airborne Networks (CROWN) solution which will generate channel and interference-aware waveforms optimized for airborne (such as UAV) networks. We will represent both the transmitter and receiver as deep neural networks, and jointly train them while taking the hardware impairments and airborne network channel and interference effects into account. We will evaluate the performance of the developed algorithms using simulations and hardware-in-the-loop network channel emulation tests.

* Information listed above is at the time of submission. *

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