# QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1910.05766 · HTML: https://mlsanity.com/paper/1910.05766/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Adversarial Machine Learning for 5G Communications Security](https://mlsanity.com/paper/2101.02656/) (2021)
- [Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation](https://mlsanity.com/paper/2410.10521/) (2024)
- [Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning](https://mlsanity.com/paper/2203.12048/) (2022)
- [Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems](https://mlsanity.com/paper/2211.01107/) (2022)
- [How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning](https://mlsanity.com/paper/2510.02265/) (2025)
- [Coordinated Anti-Jamming Resilience in Swarm Networks via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2512.16813/) (2025)

## Abstract (arXiv)

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.