QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning

Abstract

The problem of quality of service (QoS) and jammingaware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks. To ensure robust communication against jamming, an interferenceaware 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.

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“…Later, Abuzainab et al [1] proposed an interference-aware routing protocol to ensure robust communication against jamming. This protocol has the purpose of allowing nodes to avoid communication holes created by jamming attacks.…”

“…In the field of internet of things (IoT), [35] discuss the challenge of using ML-based techniques, including RL, to protect user privacy (e.g., against eavesdropping attacks [7]). In this regard, the cooperation framework previously explained in [1] also aims to make decisions on eavesdropping attacks using a dedicated deep RL approach. In another area, to protect wireless networks, Xie and Xiao [36] apply prospect theory (theory based on the observation that people react differently to potential losses and potential gains -wikipedia) to formulate the interaction between a smart attacker and a mobile user.…”

“…Bezzo [1] demonstrated the vulnerability of autonomous cyberphysical systems to attacks like sensor spoofing and used RL techniques to determine which sensors are compromised. Therefore, he proposes a reachability-based approach and a Bayesian Inverse RL technique [6] to leverage the history of sensor data and predict the attack [11].…”

An Intelligent Anti-Jamming Scheme for Cognitive Radio Based on Deep Reinforcement Learning

“…A novel channel access strategy to cope with channel jamming based on Q learning has been proposed in [22]. Literature [23] designed an interference-aware routing protocol and proposed a cooperation framework based on reinforcement learning to defend the network against jamming attacks. Since traditional Q learning is inefficient and hard to converge when the state space or action space is large, deep neural networks are adopted by reinforcement learning to achieve deep reinforcement learning which can take the spectrum waterfall as input and outputs channel selection actions [12].…”

Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification

“…Deep learning finds rich application in wireless communications. Examples include spectrum sensing [10], MIMO detection [11], channel estimation and signal detection [12], physical layer communications [13], jammer detection [14], stealth jamming [15], [16], power control [17], signal spoofing [18], and transmitter-receiver scheduling [19]. RF signal classification can support different applications such as radio fingerprinting [28] that can be ultimately used in cognitive radio systems [29] subject to dynamic and unknown interference and jamming effects [30].…”