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

TL;DR

This paper presents a comprehensive framework combining null-steering antennas and advanced AI algorithms to enhance UAV swarm resilience against jamming, ensuring stable communication and coordinated mission execution.

Contribution

It introduces a unified optimization approach integrating GA, SL, and RL for dynamic UAV swarm management with null-steering antennas for jamming mitigation.

Findings

RL with PPO offers real-time adaptability and low computational cost.

GA provides stable, collision-free trajectories but is computationally intensive.

SL models replicate GA configurations but lack generalization in dynamic scenarios.

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.