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Robust Resource Allocation in Airborne Networks (RELIANT)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-21-P-0657
Agency Tracking Number: FX20C-TCSO1-0462
Amount: $50,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF20C-TCSO1
Solicitation Number: X20.C
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-02-09
Award End Date (Contract End Date): 2021-05-08
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
 Tugba Erpek
 (301) 294-4246
Business Contact
 Mark James
Phone: (301) 294-5221
Research Institution
 Syracuse University
 Stuart Taub
900 South Crouse Ave
Syracuse, NY 13244-0000
United States

 (315) 443-9356
 Nonprofit College or University

Currently, most state-of-the-art resource utilization algorithms are based on solving optimization problems. However, such approaches may require complete information of the system parameters, which may be difficult to obtain especially in highly dynamic airborne networks, and also may lead to high complexity and scalability concerns. High complexity can make it more difficult to obtain the optimal solution in real time, weakening the practicality of optimization techniques in airborne networks. This has led to interest in learning-based radio resource allocation strategies. We will develop robust, reliable and scalable distributed deep reinforcement learning (RL)-based power control and dynamic channel access policies for airborne networks (such as UAV networks). We will first develop RL-based radio resource allocation algorithms by considering both centralized and distributed training. Then, we will consider interference effects, develop jamming and adversarial attacks aimed at minimizing the accuracy of deep RL-based resource utilization, analyze the sensitivity of deep RL-based radio resource utilization and develop defense mechanisms. We will evaluate the algorithms under scenarios that are of interest to the AFRL using simulations and hardware-in-the-loop network channel emulation tests.

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

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