  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.
