QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning
This addresses secure and efficient networking in adversarial environments, though it is incremental as it builds on existing deep reinforcement learning methods.
The paper tackles robust wireless communication under jamming and eavesdropping attacks by developing an interference-aware routing protocol and a deep reinforcement learning framework for distributed decision-making, achieving a three-fold throughput increase compared to a benchmark policy.
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.