Multi-UAV-Assisted Short-Packet Covert Communications: JointOptimization of UAV Trajectories, Cooperative Jamming, and Covert Transmit Power

WANG Libo; CHENG Lin; ZHANG Xiangyin; WANG Zhuwei; QIN Kaiyu

doi:10.11999/JEIT260210

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WANG Libo, CHENG Lin, ZHANG Xiangyin, WANG Zhuwei, QIN Kaiyu. Multi-UAV-Assisted Short-Packet Covert Communications: JointOptimization of UAV Trajectories, Cooperative Jamming, and Covert Transmit Power[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260210

Citation:

WANG Libo, CHENG Lin, ZHANG Xiangyin, WANG Zhuwei, QIN Kaiyu. Multi-UAV-Assisted Short-Packet Covert Communications: JointOptimization of UAV Trajectories, Cooperative Jamming, and Covert Transmit Power[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260210

Citation:

WANG Libo, CHENG Lin, ZHANG Xiangyin, WANG Zhuwei, QIN Kaiyu. Multi-UAV-Assisted Short-Packet Covert Communications: JointOptimization of UAV Trajectories, Cooperative Jamming, and Covert Transmit Power[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260210

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Multi-UAV-Assisted Short-Packet Covert Communications: JointOptimization of UAV Trajectories, Cooperative Jamming, and Covert Transmit Power

doi: 10.11999/JEIT260210 cstr: 32379.14.JEIT260210

1.
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
2.
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China

Funds: The National Natural Science Foundation of China (62371014), Sichuan Natural Science Foundation (2024NSFSC0176)

Received Date: 2026-02-28
Accepted Date: 2026-10-10
Rev Recd Date: 2026-09-30

Available Online: 2026-10-20

Abstract

Abstract

Objective With their high mobility, flexible deployment, and Line-of-Sight (LoS) links, Unmanned Aerial Vehicles (UAVs) are promising components of future wireless communication systems. However, the broadcast nature of LoS links exposes UAV communications to detection by malicious wardens. Covert communication is therefore used to prevent transmission activity from being detected. Meanwhile, Ultra-Reliable and Low-Latency Communications (URLLC) require short-packet (finite-blocklength) transmission, for which decoding errors are non-negligible and the infinite-blocklength assumption is unsuitable. This makes UAV-assisted short-packet covert communication an important research topic. Nevertheless, most existing studies consider a single UAV and a static environment, limiting communication service capacity and applicability in dynamic scenarios. To address this research gap, this study investigates a multi-UAV-assisted short-packet covert communication system. Methods A system model for multi-UAV-assisted short-packet covert communication is established, in which multiple UAVs serve multiple randomly moving covert users while a moving aerial warden attempts to detect their transmissions. Cooperative jammers are selected from public users to transmit jamming signals that impair the warden’s detection capability. User mobility is modeled using the Gauss-Markov random mobility model. A binary hypothesis-testing model is formulated at the warden, from which the Detection Error Probability (DEP) is derived. An optimization problem is formulated to maximize short-packet covert throughput under the covertness constraint by jointly optimizing UAV trajectories, cooperative jammer selection, and covert users’ transmit powers. A short-packet covert communication algorithm based on Multi-Agent Proximal Policy Optimization (MAPPO), termed MAPPO-SPCC, is proposed. Specifically, MAPPO is used to train multiple agents to learn coordinated UAV trajectory-control policies for dynamic environments. During execution, each agent outputs its UAV’s velocity at each time slot, thereby determining UAV trajectories and user associations. The algorithm then selects cooperative jammers and optimizes covert users’ transmit powers by solving a convex optimization problem. Results and Discussions Simulations are conducted to evaluate the performance of MAPPO-SPCC. The reward curves show stable convergence at an appropriate learning rate and indicate that training remains robust as user mobility increases progressively (Fig. 2). The loss curves further support the stability of the training process (Fig. 3). The trajectory plots show that the agents adaptively adjust UAV paths in response to the spatial distribution of covert users and the aerial warden’s patrol path (Fig. 4). The corresponding covert throughput heatmaps show that high-throughput regions overlap with UAV hovering regions (Fig. 5), indicating that the learned policies identify where UAV positioning yields greater covert throughput based on the observed environmental states. (Fig. 6) further examines how blocklength and the covertness constraint affect total covert throughput. Relaxing the covertness constraint increases achievable throughput because it permits higher transmit power. Conversely, increasing the blocklength reduces total throughput in the simulations. A larger blocklength allows the warden to collect more detection samples, improving its statistical detection capability and making the covertness constraint more stringent. Covert users must therefore reduce their transmit powers to satisfy the constraint, resulting in lower total throughput. Comparisons with the Multi-Agent Deep Deterministic Policy Gradient (MADDPG), Deep Q-Network (DQN), and Ant Colony Optimization (ACO) algorithms further show that MAPPO-SPCC consistently achieves the highest covert throughput (Fig. 7). This advantage is attributable to MAPPO-SPCC’s ability to coordinate UAV trajectories across agents and update policies on-policy as the environment changes. Conclusions This study investigates a multi-UAV-assisted short-packet covert communication system in which multiple UAVs collaboratively serve multiple randomly moving covert users while cooperative jamming impairs a moving aerial warden’s detection capability. The MAPPO-SPCC algorithm jointly optimizes UAV trajectories, cooperative jammer selection, and covert users’ transmit powers to maximize short-packet covert throughput under the covertness constraint. The simulation results show that MAPPO-SPCC adapts UAV trajectories to dynamic user and warden positions and achieves higher covert throughput than the MADDPG, DQN, and ACO baseline algorithms. Future work may integrate Physical Layer Security (PLS) with covert communication to improve both transmission covertness and content confidentiality. Extending trajectory optimization to three dimensions may further improve system flexibility and performance.

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References(35)

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