Anti-Jamming based on Null-Steering Antennas and Intelligent UAV Swarm Behavior
Miguel Lourenço, António Grilo
TL;DR
The paper tackles maintaining robust inter-UAV communication in jammed environments by proposing a unified anti-jamming framework that integrates genetic algorithms (GA), supervised learning (SL), and reinforcement learning (RL) to optimize antenna directionality, swarm formation, and path planning across epochs and timeslots. It employs null-steering antennas to suppress interference and assesses three collision rules under static and dynamic scenarios, with PPO-based RL enabling real-time adaptation. GA provides strong optimization and generates datasets used to train SL and initialize RL, though it incurs high computational cost; SL models replicate GA-like configurations but struggle with dynamic or constrained conditions. RL demonstrates adaptability and low online computation, achieving collision-free trajectories and stable communication, especially when complemented by a rotation-based Adaptive Movement Model that generalizes to arbitrary directions. The framework offers a transparent, reproducible basis for resilient swarm communication research and highlights trade-offs between optimality (GA), generalization (SL), and real-time responsiveness (RL) under jamming.
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.