QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning

Nof Abuzainab, Volkan Isler, Aylin Yener, Tugba Erpek, Kemal Davaslioglu, Yalin E. Sagduyu, Yi Shi, Sharon J. MacKey, Mitesh Patel, Frank Panettieri, Muhammad A. Qureshi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMILCOM 2019 - 2019 IEEE Military Communications Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728142807
StatePublished - Nov 2019
Event2019 IEEE Military Communications Conference, MILCOM 2019 - Norfolk, United States
Duration: Nov 12 2019Nov 14 2019

Publication series

NameProceedings - IEEE Military Communications Conference MILCOM


Conference2019 IEEE Military Communications Conference, MILCOM 2019
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2019 IEEE.


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