This repository contains the MATLAB implementation of the joint trajectory and resource optimization algorithm described in the paper:
M. Tatar Mamaghani, X. Zhou, N. Yang, and A. L. Swindlehurst, "Secure Short-Packet Communications via UAV-Enabled Mobile Relaying: Joint Resource Optimization and 3D Trajectory Design," IEEE Transactions on Wireless Communications, vol. 23, no. 7, pp. 7802-7815, July 2024.
Short-packet communication (SPC) and unmanned aerial vehicles (UAVs) are anticipated to play crucial roles in the development of 5G-and-beyond wireless networks and the Internet of Things (IoT). In this paper, we propose a secure SPC system, where a UAV serves as a mobile decode-and-forward (DF) relay, periodically receiving and relaying small data packets from a remote IoT device to its receiver in two hops with strict latency requirements, in the presence of an eavesdropper. This system requires careful optimization of important design parameters, such as the coding blocklengths of both hops, transmit powers, and UAV's trajectory. While the overall optimization problem is nonconvex, we tackle it by applying a block successive convex approximation (BSCA) approach to divide the original problem into three subproblems and solve them separately. Then, an overall iterative algorithm is proposed to obtain the final design with guaranteed convergence. Our proposed low-complexity algorithm incorporates 3D trajectory design and resource management to optimize the effective average secrecy throughput of the communication system over the course of UAV-relay's mission. Simulation results demonstrate significant performance improvements compared to various benchmark schemes and provide useful design insights on the coding blocklengths and transmit powers along the trajectory of the UAV.
The code implements an iterative block successive convex approximation (BSCA) algorithm to maximize the effective average secrecy throughput (EAST) in a UAV-relayed IoT system with finite blocklength constraints and an eavesdropper with uncertain location.
- Joint optimization of transmit powers, coding blocklengths, and 3D UAV trajectory.
- Robust design under eavesdropper location uncertainty.
- Modular subproblem solvers (power allocation, blocklength allocation, trajectory optimization).
- Support for benchmark schemes: resource-only optimization (RDFT) and trajectory-only optimization (TDFR).
- Comprehensive visualization of results.
- MATLAB (R2023b or later recommended)
- CVX (http://cvxr.com/cvx/) with a compatible solver, e.g., SDPT3, SeDuMi, Mosek (recommended)
- Clone this repository:
git clone https://github.com/yourusername/UAV-Secure-SPC.git cd UAV-Secure-SPC - Install CVX and add it to your MATLAB path.
- Run
startup.min MATLAB to add all required folders to the path.
Run a simple demo:
params = get_system_params(4); % load scenario 4 parameters
results = joint_optimization(params); % run joint optimization
visualize_system(gca, params.qa, params.qb, params.qe_est, params.qi, params.qf);
plot_uav_trajectory(gca, results.qr, 'DisplayName', 'JTRD');You can also use the provided scripts:
scripts/run_single_scenario.m– run one configuration and plot results.scripts/run_eve_uncertainty.m– study the impact of Eve's location uncertainty.scripts/run_blocklength_sweep.m– sweep over maximum blocklengthLmax.scripts/run_comparison.m– compare JTRD against RDFT and TDFR.
UAV-Secure-SPC/
├── README.md
├── LICENSE
├── .gitignore
├── startup.m
├── config/
│ └── get_system_params.m
├── src/
│ ├── algorithms/
│ │ ├── joint_optimization.m
│ │ ├── power_optimization.m
│ │ ├── blocklength_optimization.m
│ │ ├── trajectory_optimization.m
│ │ ├── resource_optimization.m
│ │ └── trajectory_only_optimization.m
│ ├── utils/
│ │ ├── compute_asb.m
│ │ ├── channel_dispersion.m
│ │ ├── func_A.m
│ │ ├── f_lb.m
│ │ ├── f_ccv.m
│ │ ├── generate_interval.m
│ │ ├── initialize_trajectory.m
│ │ ├── midway_turn_constraint.m
│ │ ├── linear_interp.m
│ │ └── pad_concatenate.m
│ └── visualization/
│ ├── visualize_system.m
│ └── plot_uav_trajectory.m
└── scripts/
├── run_single_scenario.m
├── run_eve_uncertainty.m
├── run_blocklength_sweep.m
└── run_comparison.m
Each function includes a help header describing its purpose, inputs, outputs, and references to equations in the paper. For detailed explanations, please refer to the paper.
This project is licensed under the MIT License – see the LICENSE file.
If you use this code in your research, please cite the paper:
@article{mamaghani2024secure,
author={Tatar Mamaghani, Milad and Zhou, Xiangyun and Yang, Nan and Swindlehurst, A. Lee},
journal={IEEE Transactions on Wireless Communications},
title={Secure Short-Packet Communications via UAV-Enabled Mobile Relaying: Joint Resource Optimization and 3D Trajectory Design},
year={2024},
volume={23},
number={7},
pages={7802-7815},
doi={10.1109/TWC.2023.3344802}
}For questions or issues, please open an issue on GitHub or contact the authors.
MIT License
Copyright (c) 2024 Milad Tatar Mamaghani et al.
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