# Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2104.04477 · HTML: https://mlsanity.com/paper/2104.04477/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 9 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.07845/) (2016)
- [Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond](https://mlsanity.com/paper/1903.05289/) (2019)
- [Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.07574/) (2020)
- [Cellular Connectivity for UAVs: Network Modeling, Performance Analysis and Design Guidelines](https://mlsanity.com/paper/1804.08121/) (2018)
- [Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraints](https://mlsanity.com/paper/2104.06256/) (2021)
- [Performance Analysis for Practical Unmanned Aerial Vehicle Networks with LoS/NLoS Transmissions](https://mlsanity.com/paper/1804.00811/) (2018)

## Built on by (most cited first, 3 of 3)

- [Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation](https://mlsanity.com/paper/2410.10521/) (2024)
- [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)

Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks. In this paper, we aim to find collision-free paths for multiple cellular-connected UAVs, while satisfying requirements of connectivity with ground base stations (GBSs) in the presence of a dynamic jammer. We first formulate the problem as a sequential decision making problem in discrete domain, with connectivity, collision avoidance, and kinematic constraints. We, then, propose an offline temporal difference (TD) learning algorithm with online signal-to-interference-plus-noise ratio (SINR) mapping to solve the problem. More specifically, a value network is constructed and trained offline by TD method to encode the interactions among the UAVs and between the UAVs and the environment; and an online SINR mapping deep neural network (DNN) is designed and trained by supervised learning, to encode the influence and changes due to the jammer. Numerical results show that, without any information on the jammer, the proposed algorithm can achieve performance levels close to that of the ideal scenario with the perfect SINR-map. Real-time navigation for multi-UAVs can be efficiently performed with high success rates, and collisions are avoided.