Secure Physical Layer Communications for Low-Altitude Economy Networking: A Survey

TL;DR

This survey addresses the security of physical-layer communications in the emerging Low-Altitude Economy Networking (LAENet), where LoS-dominated aerial channels expose UAVs and infrastructure to eavesdropping, jamming, spoofing, and injection. It categorizes defenses by confidentiality, availability, and integrity, detailing anti-eavesdropping and authentication methods (including convex optimization, DRL, PUFs, and PLA), anti-jamming and spoofing strategies (trajectory/power optimization and MARL), and anomaly/injection defenses (HDBN/GDBN-based detectors, SIC, and SemperFi). The paper highlights lessons learned from convex optimization, RL, and deep learning approaches, and points to future directions such as energy-efficient security, multi-UAV collaboration, AI-driven defense, space–air–ground integration, and 6G-enabled secure UAVs. Collectively, it provides a structured, technical roadmap to strengthen LAENet security, enabling scalable, reliable, and secure low-altitude connectivity for diverse urban applications.

Abstract

The Low-Altitude Economy Networking (LAENet) is emerging as a transformative paradigm that enables an integrated and sophisticated communication infrastructure to support aerial vehicles in carrying out a wide range of economic activities within low-altitude airspace. However, the physical layer communications in the LAENet face growing security threats due to inherent characteristics of aerial communication environments, such as signal broadcast nature and channel openness. These challenges highlight the urgent need for safeguarding communication confidentiality, availability, and integrity. In view of the above, this survey comprehensively reviews existing secure countermeasures for physical layer communication in the LAENet. We explore core methods focusing on anti-eavesdropping and authentication for ensuring communication confidentiality. Subsequently, availability-enhancing techniques are thoroughly discussed for anti-jamming and spoofing defense. Then, we review approaches for safeguarding integrity through anomaly detection and injection protection. Furthermore, we discuss future research directions, emphasizing energy-efficient physical layer security, multi-drone collaboration for secure communication, AI-driven security defense strategy, space-air-ground integrated security architecture, and 6G-enabled secure UAV communication. This survey may provide valuable references and new insights for researchers in the field of secure physical layer communication for the LAENet.

Figures (11)

• Figure 1: The overall architecture of the LAENet covers the main application scenarios, including emergency monitoring and response, temporary communication relay, communication coverage expansion, low-altitude smart logistics, and urban air mobility. The table compares the similarities and differences between the LAENet, single UAV, and UAV networks, representing the evolution of the LAENet.
• Figure 2: Background knowledge of the LAENet and security issues in its physical layer communication. Describe the definition of the LAENet and its communication application scenarios. Elaborate on three key metrics for secure physical layer communication: communication confidentiality, which combats eavesdropping attacks and unauthorized access; anti-jamming strategies and spoofing defense for ensuring communication availability; and anomaly detection and injection defense to prevent adversaries from compromising communication integrity.
• Figure 3: The overall architecture of the anti-eavesdropping strategy. Part A illustrates the system model against fixed ground eavesdroppers. In this setup, one UAV operates as a mobile server, while another UAV serves as a jammer to emit jamming signals to disrupt the eavesdroppers' interception capabilities. Part B presents the system model for flying eavesdroppers, where one UAV acts as the server, and another UAV functions as a mobile eavesdropper. To mitigate eavesdropping risks, a ground-based jammer actively emits interference signals to secure communications.
• Figure 4: The overall architecture of the RL for anti-eavesdropping. Part A describes the DDQN-based scheme, where the system state is used to generate actions through the DDQN network, followed by action execution and obtaining the next state and reward. An experience replay mechanism is employed to store and randomly sample training data. Part B presents the DDPG-based scheme, where actions are generated through Actor and Critic networks, interacting with the environment to obtain rewards. An experience replay buffer is used to store and sample mini-batches. Part C describes the MADDPG-based scheme, involving multiple UAV agents, each with its own Actor and Critic networks, interacting with the environment and sharing rewards. Part D showcases the MAPPO-LSTM-based scheme, where Actor and Critic networks with LSTM layers process time-series data and train through an experience replay buffer.
• Figure 5: The overall architecture illustrates various deep learning-based architectures designed to enhance anti-eavesdropping capabilities in UAV deployment scenarios. Part A presents a DNN-based architecture that processes air-ground and ground-ground channel states to determine UAV deployment. Part B shows an FL-DNN-based architecture, incorporating modules for reinforcement learning, DNN-based feature mapping, and FL. Part C depicts an MD-GAN-based architecture, where a generator produces trajectories and power outputs based on location and environment status, while a discriminator evaluates the decisions. Part D introduces a DD-GAN-based architecture, focusing on generating jamming solutions to maximize covert rates, with a discriminator distinguishing between jamming and non-jamming solutions. Part E illustrates a GDMTD3-based architecture, utilizing an experience replay buffer and diffusion reverse process to optimize UAV deployment strategies.