Securing 5G and Beyond-Enabled UAV Networks: Resilience Through Multiagent Learning and Transformers Detection

Achieving resilience remains a significant challenge for Unmanned Aerial Vehicle (UAV) communications in 5G and 6G networks. Although UAVs benefit from superior positioning capabilities, rate optimization techniques, and extensive line-of-sight (LoS) range, these advantages alone cannot guarantee high reliability across diverse UAV use cases. This limitation becomes particularly evident in urban environments, where UAVs face vulnerability to jamming attacks and where LoS connectivity is frequently compromised by buildings and other physical obstructions. This paper introduces DET-FAIR- WINGS ( Detection-Enhanced Transformer Framework for AI-Resilient Wireless Networks in Ground UAV Systems), a novel solution designed to enhance reliability in UAV communications under attacks. Our system leverages multi-agent reinforcement learning (MARL) and transformer-based detection algorithms to identify attack patterns within the network and subsequently select the most appropriate mechanisms to strengthen reliability in authenticated UAV-Base Station links. The DET-FAIR-WINGS approach integrates both discrete and continuous parameters. Discrete parameters include retransmission attempts, bandwidth partitioning, and notching mechanisms, while continuous parameters encompass beam angles and elevations from both the Base Station (BS) and user devices. The detection part integrates a transformer in the agents to speed up training. Our findings demonstrate that replacing fixed retransmission counts with AI-integrated flexible approaches in 5G networks significantly reduces latency by optimizing decision-making processes within 5G layers.

PCA-Featured Transformer for Jamming Detection in 5G UAV Networks

Jamming attacks pose a threat to Unmanned Aerial Vehicle (UAV) wireless communication systems, potentially disrupting essential services and compromising network reliability. Current detection approaches struggle with sophisticated artificial intelligence (AI) jamming techniques that adapt their patterns while existing machine learning solutions often require extensive feature engineering and fail to capture complex temporal dependencies in attack signatures. Furthermore, 5G networks using either Time Division Duplex (TDD) or Frequency Division Duplex (FDD) methods can face service degradation from intentional interference sources. To address these challenges, we present a novel transformer-based deep learning framework for jamming detection with Principal Component Analysis (PCA) added features. Our architecture leverages the transformer's self-attention mechanism to capture complex temporal dependencies and spatial correlations in wireless signal characteristics, enabling more robust jamming detection techniques. The U-shaped model incorporates a modified transformer encoder that processes signal features including received signal strength indicator (RSSI) and signal-to-noise ratio (SINR) measurements, alongside a specialized positional encoding scheme that accounts for the periodic nature of wireless signals. In addition, we propose a batch size scheduler and implement chunking techniques to optimize training convergence for time series data. These advancements contribute to achieving up to a ten times improvement in training speed within the advanced U-shaped encoder-decoder model introduced. Simulation results demonstrate that our approach achieves a detection accuracy of 90.33 \% in Line-of-Sight (LoS) and 84.35 % in non-Line-of-Sight (NLoS) and outperforms machine learning methods and existing deep learning solutions such as the XGBoost (XGB) classifier in approximately 4%.