TY - GEN
T1 - QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning
AU - Abuzainab, Nof
AU - Isler, Volkan
AU - Yener, Aylin
AU - Erpek, Tugba
AU - Davaslioglu, Kemal
AU - Sagduyu, Yalin E.
AU - Shi, Yi
AU - MacKey, Sharon J.
AU - Patel, Mitesh
AU - Panettieri, Frank
AU - Qureshi, Muhammad A.
N1 - Publisher Copyright:
© 2019 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019/11
Y1 - 2019/11
N2 - The problem of quality of service (QoS) and jamming-aware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks. To ensure robust communication against jamming, an interference-aware routing protocol is developed that allows nodes to avoid communication holes created by jamming attacks. Then, a distributed cooperation framework, based on deep reinforcement learning, is proposed that allows nodes to assess network conditions and make deep learning-driven, distributed, and real-time decisions on whether to participate in data communications, defend the network against jamming and eavesdropping attacks, or jam other transmissions. The objective is to maximize the network performance that incorporates throughput, energy efficiency, delay, and security metrics. Simulation results show that the proposed jamming-aware routing approach is robust against jamming and when throughput is prioritized, the proposed deep reinforcement learning approach can achieve significant (measured as three-fold) increase in throughput, compared to a benchmark policy with fixed roles assigned to nodes.
AB - The problem of quality of service (QoS) and jamming-aware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks. To ensure robust communication against jamming, an interference-aware routing protocol is developed that allows nodes to avoid communication holes created by jamming attacks. Then, a distributed cooperation framework, based on deep reinforcement learning, is proposed that allows nodes to assess network conditions and make deep learning-driven, distributed, and real-time decisions on whether to participate in data communications, defend the network against jamming and eavesdropping attacks, or jam other transmissions. The objective is to maximize the network performance that incorporates throughput, energy efficiency, delay, and security metrics. Simulation results show that the proposed jamming-aware routing approach is robust against jamming and when throughput is prioritized, the proposed deep reinforcement learning approach can achieve significant (measured as three-fold) increase in throughput, compared to a benchmark policy with fixed roles assigned to nodes.
UR - http://www.scopus.com/inward/record.url?scp=85082399181&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082399181&partnerID=8YFLogxK
U2 - 10.1109/MILCOM47813.2019.9020985
DO - 10.1109/MILCOM47813.2019.9020985
M3 - Conference contribution
AN - SCOPUS:85082399181
T3 - Proceedings - IEEE Military Communications Conference MILCOM
BT - MILCOM 2019 - 2019 IEEE Military Communications Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Military Communications Conference, MILCOM 2019
Y2 - 12 November 2019 through 14 November 2019
ER -