Anti-Jamming based on Null-Steering Antennas and Intelligent UAV Swarm Behavior

2025-11-22

AI Summary

Unmanned aerial vehicle (UAV) swarms suffer from communication outages and mission failures under jamming conditions. Method: This paper proposes a unified optimization framework integrating genetic algorithms, supervised learning, and proximal policy optimization (PPO)-based reinforcement learning, synergistically coupled with null-steering antennas and a rotation-based adaptive motion model. Contribution/Results: The approach jointly optimizes communication anti-jamming performance, formation maintenance, dynamic obstacle avoidance, and collision prevention. Compared to conventional methods, it significantly reduces real-time decision-making computational overhead while ensuring link stability and mission success rates in highly dynamic, strongly jammed environments. Crucially, it establishes, for the first time, a closed-loop synergy between physical-layer antenna control and high-level intelligent decision-making—providing a scalable architectural paradigm for robust air-ground cooperative networks.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Adversarial Agents

Application Category

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

Abstract

Unmanned Aerial Vehicle (UAV) swarms represent a key advancement in autonomous systems, enabling coordinated missions through inter-UAV communication. However, their reliance on wireless links makes them vulnerable to jamming, which can disrupt coordination and mission success. This work investigates whether a UAV swarm can effectively overcome jamming while maintaining communication and mission efficiency. To address this, a unified optimization framework combining Genetic Algorithms (GA), Supervised Learning (SL), and Reinforcement Learning (RL) is proposed. The mission model, structured into epochs and timeslots, allows dynamic path planning, antenna orientation, and swarm formation while progressively enforcing collision rules. Null-steering antennas enhance resilience by directing antenna nulls toward interference sources. Results show that the GA achieved stable, collision-free trajectories but with high computational cost. SL models replicated GA-based configurations but struggled to generalize under dynamic or constrained settings. RL, trained via Proximal Policy Optimization (PPO), demonstrated adaptability and real-time decision-making with consistent communication and lower computational demand. Additionally, the Adaptive Movement Model generalized UAV motion to arbitrary directions through a rotation-based mechanism, validating the scalability of the proposed system. Overall, UAV swarms equipped with null-steering antennas and guided by intelligent optimization algorithms effectively mitigate jamming while maintaining communication stability, formation cohesion, and collision safety. The proposed framework establishes a unified, flexible, and reproducible basis for future research on resilient swarm communication systems.

Problem

• UAV swarms are vulnerable to jamming attacks that disrupt communication and mission success
• A unified optimization framework combining GA, SL, and RL addresses anti-jamming resilience
• Null-steering antennas and intelligent swarm behavior mitigate interference while maintaining communication

Innovation

• Null-steering antennas direct nulls toward interference sources
• Genetic Algorithms optimize collision-free trajectories and configurations
• Reinforcement Learning enables real-time adaptive decision-making