# Achieving Hiding and Smart Anti-Jamming Communication: A Parallel DRL Approach against Moving Reactive Jammer

arXiv: https://arxiv.org/abs/2502.02385 · HTML: https://mlsanity.com/paper/2502.02385/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 54 references that are in the map.
- First posted on arXiv 4 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [Exploration in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2205.00824/) (2022)
- [On Empirical Comparisons of Optimizers for Deep Learning](https://mlsanity.com/paper/1910.05446/) (2019)
- [Convolutional Neural Networks at Constrained Time Cost](https://mlsanity.com/paper/1412.1710/) (2014)
- [Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.00292/) (2021)
- [t-Soft Update of Target Network for Deep Reinforcement Learning](https://mlsanity.com/paper/2008.10861/) (2020)
- [Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration](https://mlsanity.com/paper/2209.07376/) (2022)
- [Game-theoretic Learning Anti-jamming Approaches in Wireless Networks](https://mlsanity.com/paper/2207.00159/) (2022)

## Abstract (arXiv)

This paper addresses the challenge of anti-jamming in moving reactive jamming scenarios. The moving reactive jammer initiates high-power tracking jamming upon detecting any transmission activity, and when unable to detect a signal, resorts to indiscriminate jamming. This presents dual imperatives: maintaining hiding to avoid the jammer's detection and simultaneously evading indiscriminate jamming. Spread spectrum techniques effectively reduce transmitting power to elude detection but fall short in countering indiscriminate jamming. Conversely, changing communication frequencies can help evade indiscriminate jamming but makes the transmission vulnerable to tracking jamming without spread spectrum techniques to remain hidden. Current methodologies struggle with the complexity of simultaneously optimizing these two requirements due to the expansive joint action spaces and the dynamics of moving reactive jammers. To address these challenges, we propose a parallelized deep reinforcement learning (DRL) strategy. The approach includes a parallelized network architecture designed to decompose the action space. A parallel exploration-exploitation selection mechanism replaces the varepsilon -greedy mechanism, accelerating convergence. Simulations demonstrate a nearly 90% increase in normalized throughput.