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

arXiv: https://arxiv.org/abs/2412.15312 · HTML: https://mlsanity.com/paper/2412.15312/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 62 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017)
- [UAV Communications for 5G and Beyond: Recent Advances and Future Trends](https://mlsanity.com/paper/1901.06637/) (2019)
- [An Overview of 5G Advanced Evolution in 3GPP Release 18](https://mlsanity.com/paper/2201.01358/) (2022)
- [What Will the Future of UAV Cellular Communications Be? A Flight from 5G to 6G](https://mlsanity.com/paper/2105.04842/) (2021)
- [A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication](https://mlsanity.com/paper/2003.07308/) (2020)
- [Cellular Connectivity for UAVs: Network Modeling, Performance Analysis and Design Guidelines](https://mlsanity.com/paper/1804.08121/) (2018)
- [Deep Attention Recognition for Attack Identification in 5G UAV scenarios: Novel Architecture and End-to-End Evaluation](https://mlsanity.com/paper/2303.12947/) (2023)
- [Detection and blind channel estimation for UAV-aided wireless sensor networks in smart cities under mobile jamming attack](https://mlsanity.com/paper/2105.11868/) (2021)
- [A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters](https://mlsanity.com/paper/2211.09706/) (2022)
- [A Service-Based Architecture for enabling UAV enhanced Network Services](https://mlsanity.com/paper/2201.05454/) (2022)
- [Accurate and Reliable Methods for 5G UAV Jamming Identification With Calibrated Uncertainty](https://mlsanity.com/paper/2211.02924/) (2022)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) face significant security risks from jamming attacks, which can compromise network functionality. Traditional detection methods often fall short when confronting AI-powered jamming that dynamically modifies its behavior, while contemporary machine learning approaches frequently demand substantial feature engineering and struggle with temporal patterns in attack signatures. The vulnerability extends to 5G networks employing Time Division Duplex (TDD) or Frequency Division Duplex (FDD), where service quality may deteriorate due to deliberate interference. We introduce a novel U-shaped transformer architecture that leverages Principal Component Analysis (PCA) to refine feature representations for improved wireless security. The training process is regularized by incorporating the output entropy uncertainty into the loss function, a mechanism inspired by the Soft Actor-Critic (SAC) algorithm in Reinforcement Learning (RL) to enable robust jamming detection techniques. The architecture features a modified transformer encoder specially designed to process critical wireless signal features, including Received Signal Strength Indicator (RSSI) and Signal-to- Interference-plus-Noise Ratio (SINR) measurements. We complement this with a custom positional encoding mechanism that specifically accounts for the inherent periodicity of wireless signals,enabling a more accurate representation of temporal signal patterns. In addition, we propose a batch size scheduler and implement chunking techniques to optimize convergence for time series data. These advancements contribute to up to a ten times improvement in training speed within the advanced U-shaped encoder-decoder transformer model introduced in this study. Experimental evaluations demonstrate the effectiveness of our entropy-based approach, achieving detection rates of 85.06% in NLoS scenarios.