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

2025-08-11

AI Summary

In dynamic adversarial environments, multi-source interference severely degrades the reliability of unmanned aerial vehicle (UAV) communications. Method: This paper proposes an agent-based autonomous anti-jamming architecture, formally defining the “UAV Intelligent Anti-Jamming Agent” and establishing a perception-decision-action (P-D-A) closed-loop framework. It integrates game-theoretic modeling of air-ground adversarial interactions with deep reinforcement learning to jointly realize spectrum sensing, policy optimization, and real-time decision-making. Contribution/Results: The work systematically evaluates the applicability boundaries of existing anti-jamming techniques and identifies critical bottlenecks hindering practical deployment. It delivers a scalable theoretical model and a deployable technical pathway for highly resilient UAV communication systems, advancing both foundational understanding and engineering implementation of intelligent anti-jamming capabilities.

Technology Category

• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Addressing multi-source interference in UAV adversarial communications
• Developing agent-based autonomous anti-jamming techniques using P-D-A framework
• Modeling UAV-jammer interactions through game theory and reinforcement learning

Innovation

• Agent-based autonomous anti-jamming techniques
• Closed-loop Perception-Decision-Action framework
• Game theory and reinforcement learning integration