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 the 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 robust trajectory design and resource management to optimize the effective average secrecy throughput of the communication system over the course of the 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. 

Secure Short-Packet Communications via UAV-Enabled Mobile Relaying: Joint Resource Optimization and 3D Trajectory Design

The combination of simultaneously transmitting and reflecting-reconfigurable intelligent surface (STAR-RIS) and nonorthogonal multiple access (NOMA) brings the necessary full-space degrees of freedom and spatial multiplexing gains for the Internet of Things (IoT) networks. The inherent network heterogeneity and sharing of wireless channels may however increase the exposure of the information interactions to the third party. To address this issue, we propose a covert communication scheme in STAR-RIS assisted NOMA networks over Nakagami-m fading channels, where both downlink and uplink IoT scenarios are considered. Under the NOMA protocol with imperfect successive interference cancelation (SIC), an IoT access point interacts with two IoT users aided by a STAR-RIS without being detected by two wardens. In this scenario, the two IoT users are located on both sides of the STAR-RIS which adopts coherent phase shifting and operates according to the mode switching protocol. To evaluate the wardens’ detection performance, the Kullback-Leibler (KL) divergence is used. Furthermore, the cascaded channel gains of IoT users and wardens are, respectively, characterized as Gamma and complex Gaussian random variables. The closed-form expressions of the expectations of KL divergence and the interruption probabilities for downlink and uplink are derived. To further improve the performance, we formulate the effective covert rate maximization as the joint optimization problems of the transmit power and power allocation coefficient for downlink and uplink, subject to the constraints for covertness, reliability and power budget, which are, respectively, resolved analytically. Extensive simulation results indicate that the proposed scheme improves covertness compared with the benchmarks.

Covert Communications in STAR-RIS Assisted NOMA IoT Networks over Nakagami-m Fading Channels

This correspondence investigates the robust unmanned aerial vehicle (UAV) enabled covert communication system with active ground wardens of collusive detection and location uncertainty. The three-dimensional (3D) trajectory and transmit power of UAV are jointly optimized to maximize the average covert transmission rate subject to the worst-case covertness constraint. The problem is intractable due to its nonconvexity and semi-positive definite matrix constraint, and an iterative suboptimal algorithm is proposed based on the block coordinate descent, S-procedure and successive convex optimization to tackle it. Numerical results demonstrate that the proposed scheme is effective and robust for UAV-enabled covert communications against active wardens.

UAV-Enabled Robust Covert Communication Against Active Wardens

Aiming at the task allocation problem of multi-UAV cooperative air combat system in battlefield environment, this paper proposes a task allocation strategy based on parameter adaptive particle swarm optimization algorithm (PAPSO). Firstly, the task allocation problem of UAV is described in the actual battlefield environment, and the mathematical model of task allocation is established based on the hit cost, damage cost and task revenue. Then, the correlation analysis method is used to assess the threat of the air combat situation of both enemy and us, and the obtained comprehensive threat matrix is used to calculate the task allocation model and serve as the basis of task allocation. Finally, a PAPSO algorithm based on evolutionary game theory is designed to solve the model. In this algorithm, a new state update formula is designed for each particle to prevent the particles from falling into iteration stagnation too early, and the evolutionary stability strategy in evolutionary game theory is introduced to adaptively update the control parameters of the algorithm, which balances the global and local search capabilities of the algorithm well, and a relaxation mechanism is also designed to reduce the loss of solution diversity in the early iteration. In this paper, a comparison experiment is designed to verify the proposed task allocation strategy. The simulation results illustrate that the proposed strategy in this paper has improved the quality of solutions and convergence speed compared to traditional strategies, which verifies its effectiveness and superiority.

A Practical Task Allocation Strategy Based on Parameter Adaptive Particle Swarm Optimization

In this paper, we study a novel joint information-theoretic secrecy and covertness model for an unmanned aerial vehicle (UAV)-assisted finite blocklength transmission system, in which an UAV delivers classified information to a ground user (Bob) in the presence of two unauthorized users (Eve and Willie). Specifically, Eve tries to decode the legitimate information while Willie tries to detect if the UAV's transmission is present or not. Firstly, we assume that the perfect location knowledge of both Eve and Willie is known at the UAV. In this scenario, to guarantee security and covertness, the average secrecy rate maximization problem is formulated via joint design of the UAV's transmit power and three dimensional (3D) trajectory under Willie's covertness constraint. Technically speaking, the optimization problem is non-convex and mathematically intractable to tackle owing to the coupling of multiple variables. To address this non-convex problem, an iterative alternating optimization (AO) algorithm is developed with the assistance of the successive convex approximation (SCA) technique. After that, we extend the system model to both Eve's and Willie's imperfect location knowledge case, which is more sophisticated compared to the perfect location knowledge case due to the uncertain location constraints. We still utilize the iterative AO algorithm combined with SCA and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {S}$</tex-math></inline-formula> -procedure techniques to cope with the nonconvex problem. Numerical results indicate that our proposed 3D optimization algorithm performs better than the conventional two dimensional (2D) scheme and the benchmark scheme for both perfect and imperfect location knowledge cases. 

Joint Information-Theoretic Secrecy and Covertness for UAV-assisted Wireless Transmission With Finite Blocklength

The Unmanned Aerial Vehicle (UAV) swarm collaborative operations is one of the disruptive technologies in future information warfare. Considering the characteristics of UAV swarm collaborative operation, we propose two UAV swarm cooperative situation awareness (SA) theory models based on the classical ones and the composition of UAV swarm. Firstly, the improved Endsley three-level SA model is proposed, in which the traditional mental model is changed into human machine intelligent model on the individual level. Then the homogeneous swarm and heterogeneous swarm cooperative SA model are separately built based on the improved team SA model and Distributed SA (DSA) model. Meanwhile, a comparison between the multi-UAV and UAV swarm is carried. Finally, a scenario analysis of swarm cooperative SA in combat situation is presented.

https://iopscience.iop.org/article/10.1088/1742-6596/2504/1/012013/pdf

UAV Swarm Cooperative Situation Awareness Theory Model Construction and Scenario Analysis

Multi-target threat assessment is an important prerequisite for jamming resource allocation and operational preparations. The more accurate the threat assessment is, the better the effect of decision support will be. In the face of complex battlefield environment and massive data, enough threat assessment attributes are conducive to improving the accuracy and credibility of the threat assessment. However, it also generates a huge amount of computation, which may cause huge challenge for both hardware and software to get the required results in a limited time. Thus, an effective attribute reduction method is proposed. Firstly, enough evaluation attributes are selected for specific threat targets. The attributes are reduced by analytic network process (AHP), the minimum variance method is used to optimize the attribute data, and then the rough set theory is applied to further optimize the threat evaluation attributes. Finally, the rationality and effectiveness are illustrated by an example of air target threat assessment

Multi-target Threat Assessment Method Based on An Effective Reduction Method

In threat assessment method, the relationships among indicators considering insufficiency and weights set unreasonable, the threat assessment method via network model put forward, used for coordinate target recognition system based on secondary radar. Firstly, the indicators were given by hierarchical structure, thinking of the relationships among indexes, then giving the indicators by network model. Then making standardization of indicators, solving the indicators weights by Analytic Network Process (ANP). Getting the systems' threat degree through threat degree assessment model, then made sequencing. Finally an example is used to verify the method, shows it is reasonable and effective. And the result is helping for allocation of interfere resources and operational deployment in later.

Cooperative Target Recognition System Threat Assessment Based on Network Model

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Papers

Joint Information-Theoretic Secrecy and Covertness for UAV-assisted Wireless Transmission With Finite Blocklength

UAV Swarm Cooperative Situation Awareness Theory Model Construction and Scenario Analysis

Multi-target Threat Assessment Method Based on An Effective Reduction Method

Cooperative Target Recognition System Threat Assessment Based on Network Model