QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based framework for robust, jamming-aware wireless communication that improves network performance, security, and resilience against adversarial attacks in dynamic environments.

Contribution

It presents a novel distributed deep reinforcement learning approach for real-time decision-making in jamming and eavesdropping scenarios, enhancing QoS and security.

Findings

Three-fold increase in throughput compared to fixed-role policies

Robustness of routing protocol against jamming attacks

Significant improvements in network security and efficiency

Abstract

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.