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

TL;DR

This survey reviews agent-based anti-jamming strategies for UAV communications, emphasizing game theory and reinforcement learning to enhance resilience against adversarial interference in dynamic environments.

Contribution

It formalizes the concept of intelligent anti-jamming agents and introduces a comprehensive decision-making framework for UAV communication resilience.

Findings

Game theory models UAV-jammer interactions.

Reinforcement learning enables adaptive anti-jamming strategies.

Identifies challenges and future directions in anti-jamming research.

Abstract

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.