# Agent-Based Anti-Jamming Techniques for UAV Communications in Adversarial Environments: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2508.11687 · HTML: https://mlsanity.com/paper/2508.11687/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2025.

## Built on by (most cited first, 8 of 8)

- [FineState-Bench: Benchmarking State-Conditioned Grounding for Fine-grained GUI State Setting](https://mlsanity.com/paper/2604.27974/) (2026)
- [TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards](https://mlsanity.com/paper/2605.19320/) (2026)
- [ServImage: An Image Generation and Editing Benchmark from Real-world Commercial Imaging Services](https://mlsanity.com/paper/2604.24023/) (2026)
- [Zero-Parameter Geometric Gating for Temporally Stable Low-Altitude UAV Video Semantic Segmentation](https://mlsanity.com/paper/2606.09162/) (2026)
- [LabGuard: Grounding Natural-Language Laboratory Rules into Runtime Guards for Embodied Laboratory Agents](https://mlsanity.com/paper/2606.31045/) (2026)
- [GAAT: Geometry-Aware Alignment Transformer for Multimodal UAV Perception](https://mlsanity.com/paper/2608.27971/) (2026)
- [Deep Q-Network Based Resilient Drone Communication:Neutralizing First-Order Markov Jammers](https://mlsanity.com/paper/2601.06095/) (2026)
- [EgoSIS: From Factorized Visual Ego-Transitions to Motion-Canonical Spatial Evidence for UAV Reasoning](https://mlsanity.com/paper/2609.08938/) (2026)

## Abstract (arXiv)

Unmanned Aerial Vehicle communications are encountering increasingly severe multi-source interference challenges in dynamic adversarial environments, which impose higher demands on their reliability and resilience. To address these challenges, agent-based autonomous anti-jamming techniques have emerged as a crucial research direction. This paper presents a comprehensive survey that first formalizes the concept of intelligent anti-jamming agents for UAV communications and establishes a closed-loop decision-making framework centered on the "Perception-Decision-Action" (P-D-A) paradigm. Within this framework, we systematically review key technologies at each stage, with particular emphasis on employing game theory to model UAV-jammer interactions and integrating reinforcement learning-based intelligent algorithms to derive adaptive anti-jamming strategies. Furthermore, we discuss potential limitations of current approaches, identify critical engineering challenges, and outline promising future research directions, aiming to provide valuable references for developing more intelligent and robust anti-jamming communication systems for UAVs.