Secure Communication in UAV–RIS-Empowered Multiuser Networks: Joint Beamforming, Phase Shift, and UAV Trajectory Optimization

In this paper, we study the maximization of the secrecy throughput of an air-to-ground UAV communication system through UAV trajectory optimization, in a scenario where multiple cooperating eavesdroppers exist. Two cooperative eavesdropping strategies, namely Selection Combing-Cooperative Eavesdropping (SC-CE) and Maximal Ratio Combining-Cooperative Eavesdropping (MRC-CE) are considered for the cooperating eavesdroppers. Based on the mathematical forms of the formulated optimization problems corresponding to the two cooperative eavesdropping schemes, we propose to use the Trajectory Increment Iteration (TII) algorithm and the Particle Swarm Optimization (PSO) algorithm to solve for the optimal trajectory, respectively. Simulation results show that by optimizing the UAV trajectory against cooperative eavesdropping, the physical layer security performance of the system can be effectively improved.

To overcome the limitations of ground-based pseudolite systems susceptible to terrain obstructions, this paper proposes an air-ground collaborative regional radio positioning system (AGCRRPS) in GNSS-denied environments, including architecture, evaluation metrics, and a systematic UAV swarm trajectory optimization method. The AGCRRPS operates through sequential pseudorange broadcasting and positioning among ground anchors, aerial UAV swarms, and ground users to ensure the stable operation of the system. The UAV swarm’s trajectory design directly impacts the propagation of system accuracy. To characterize the error propagation in this layered air-ground structure, we derive a precision mapping model suitable for multi-layer systems and the equivalent Dilution of Precision (DOP) metric for instantaneous geometric configurations. Based on this, we formulate a multi-objective optimization function that effectively evaluates regional positioning systems, incorporating three dimensions: average accuracy, inter-user balance, and temporal stability. An adaptive trajectory representation protocol that optimizes UAV trajectories while preserving primary task objectives is developed. By leveraging the Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the multi-objective optimizer, we establish a systematic UAV swarm trajectory optimization approach, with simulation results demonstrating its effectiveness. Further simulations reveal the precision limits and inter-layer independence in multi-layer systems, providing theoretical insights for weak-anchor multi-tier navigation system design. Additionally, the influence of resource allocation on user performance is analyzed, offering practical guidance for anchor-UAV ratio configurations in engineering deployments.

Unmanned aerial vehicles (UAVs) with the potential of providing reliable high-rate connectivity, are becoming a promising component of future wireless networks. A UAV collects data from a set of randomly distributed sensors, where both the locations of these sensors and their data volume to be transmitted are unknown to the UAV. In order to assist the UAV in finding the optimal motion trajectory in the face of the uncertainty without the above knowledge whilst aiming for maximizing the cumulative collected data, we formulate a reinforcement learning problem by modelling the motion-trajectory as a Markov decision process with the UAV acting as the learning agent. Then, we propose a pair of novel trajectory optimization algorithms based on stochastic modelling and reinforcement learning, which allows the UAV to optimize its flight trajectory without the need for system identification. More specifically, by dividing the considered region into small tiles, we conceive state-action-reward-state-action (Sarsa) and Q-learning based UAV-trajectory optimization algorithms (i.e., SUTOA and QUTOA) aiming to maximize the cumulative data collected during the finite flight-time. Our simulation results demonstrate that both of the proposed approaches are capable of finding an optimal trajectory under the flight-time constraint. The preference for QUTOA vs. SUTOA depends on the relative position of the start and the end points of the UAVs.

Most UAVs depend on realtime 3D point clouds to navigate through unknown environments. However, both point clouds processing and trajectory planning are computationally expensive and will deplete UAV's battery quickly. There are also inevitable uncertainties in point clouds, which further makes collision-free trajectory planning a very challenging problem. To address these issues, we propose an <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>e</u></b> nergy-efficient and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>c</u></b> loud-assisted aut <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>onom</u></b> ous navigation s <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>y</u></b> stem, called ECONOMY, which allows a UAV to transmit the point clouds through a cellular network to a computing cloud that plans the trajectories for the UAV in realtime. To maximize the UAV's energy-efficiency, we jointly optimize its velocity, trajectory, and transmission power. Since the formulated problem is non-convex, we decompose it into a UAV trajectory and velocity optimization problem (UTVO), and a UAV communication optimization problem (UCO), which are resolved by an intelligent solver built upon the Suggest-and-Improve framework, and gradient ascent, respectively. To address point clouds uncertainties, we devise probabilistic collision-free constraints which can be handled in a deterministic manner by exploiting the tight <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(n,2,\mathcal {R}^n)$</tex-math></inline-formula> -upper bound for convex sets. Simulation results for a UAV exploring a simulated urban environment demonstrate the efficacy and efficiency of ECONOMY.

Joint beamforming and phase-shifting design for energy efficiency in RIS-assisted MISO communication with statistical CSI

This work investigates the potential of the aerial intelligent reflecting surface (AIRS) in secure communication, where an intelligent reflecting surface (IRS) carried by an unmanned aerial vehicle (UAV) is utilized to help the communication between the ground nodes. Specifically, we formulate the joint design of the AIRS's deployment and the phase shift to maximize the secrecy rate. To solve the non-convex objective, we develop an alternating optimization (AO) approach, where the phase shift optimization is solved by the Riemannian manifold optimization (RMO) method, while the deployment optimization is handled by the successive convex approximation (SCA) technique. Furthermore, to reduce the computational complexity of the RMO method, an element-wise block coordinate descent (EBCD) based method is employed. Simulation results verify the effect of AIRS in improving the communication security, as well as the importance of designing the deployment and phase shift properly.

In this paper, the control of chaotic externalinjection multiplequantumwell laser synchronization via phasecontrollers is presented and external chaos phase shift keying modulation is proposed for secure communication. Chaotic laser synchronization is numerically achieved under controlling by phasecontrollers. Lasing phase shift is controlled via continuous ON/ON chaos shift keying and chaotic laser signal is transmitted by chaos phase shift keying modulation on end. Chaotic laser demodulation is realized by setting a phase shift of the receiver phasecontrollers. ON/ON chaos phase shift keying communications at bit rates of 01 and 05Gbit/s are numerically simulated. ON/ON chaos phase shift keying communication bit rate，the synchronization characterization and the system parameter mismatch are numerically analyzed, in particular.

To meet the joint requirements of security and covertness in high-speed railway communications, this letter proposes an irregular reconfigurable intelligent surface (IRIS)-assisted scheme integrating physical layer security and covert communications. A mobile relay is deployed to mitigate penetration loss, and a joint optimization framework is developed, where IRIS topology is learned via graph neural networks, covert constraints are equivalently transformed, and metareinforcement learning jointly optimizes beamforming and phase shifts. Simulation results demonstrate superior robustness under stringent covertness requirements compared with conventional RIS-based schemes.

This article studies secure communication in an Internet of Things (IoT) network, where the confidential signal is sent by an active refracting reconfigurable intelligent surface (RIS)-based transmitter, and a passive reflective RIS is utilized to improve the secrecy performance of users in the presence of multiple eavesdroppers. Specifically, we aim to maximize the weighted sum secrecy rate by jointly designing the power allocation, transmit beamforming (BF) of the refracting RIS, and the phase shifts of the reflective RIS. To solve the nonconvex optimization problem, we propose a linearization method to approximate the objective function into a linear form. Then, an alternating optimization (AO) scheme is proposed to jointly optimize the power allocation factors, BF vector, and phase shifts, where the first one is found using the Lagrange dual method, while the latter two are obtained by utilizing the penalty dual decomposition method. Moreover, considering the demands of green and secure communications, by applying Dinkelbach’s method, we extend our proposed scheme to solving a secrecy energy maximization problem. Finally, simulation results demonstrate the effectiveness of the proposed design.

In complex urban wireless environments, unmanned aerial vehicle–mobile edge computing (UAV-MEC) systems face challenges like link blockage and single-antenna eavesdropping threats. The traditional single reconfigurable intelligent surface (RIS), limited in collaboration, struggles to address these issues. This paper proposes a double-RIS cooperative UAV-MEC optimization scheme, leveraging their joint reflection to build multi-dimensional signal paths, boosting legitimate link gains while suppressing eavesdropping channels. It considers double-RIS phase shifts, ground user (GU) transmission power, UAV trajectories, resource allocation, and receiving beamforming, aiming to maximize secure energy efficiency (EE) while ensuring long-term stability of GU and UAV task queues. Given random task arrivals and high-dimensional variable coupling, a dynamic model integrating queue stability and secure transmission constraints is built using Lyapunov optimization, transforming long-term stochastic optimization into slot-by-slot deterministic decisions via the drift-plus-penalty method. To handle high-dimensional continuous spaces, an end-to-end proximal policy optimization (PPO) framework is designed for online learning of multi-dimensional resource allocation and direct acquisition of joint optimization strategies. Simulation results show that compared with benchmark schemes (e.g., single RIS, non-cooperative double RIS) and reinforcement learning algorithms (e.g., advantage actor–critic (A2C), deep deterministic policy gradient (DDPG), deep Q-network (DQN)), the proposed scheme achieves significant improvements in secure EE and queue stability, with faster convergence and better optimization effects, fully verifying its superiority and robustness in complex scenarios.

In this paper, we investigate the joint design of beamforming vectors and trajectory for unmanned aerial vehicles (UAVs) in integrated sensing and communications networks, aiming to maximize the achievable covert rate (ACR) for legitimate users against multiple passive wardens. Considering the worst-case scenario, where the wardens strategically select optimal decision thresholds, we derive the minimum detection error probability and incorporate covertness constraints within the beamforming scheme. Our approach entails formulating the design as a non-convex optimization problem for maximizing the average ACR along the UAV trajectory. The formulation takes into account various practical constraints, such as the maximum transmit power, UAV flight speed limitations, minimum beamforming gain towards sensing targets, and the detection probability threshold for wardens. To address this intricate problem, we propose a block coordinate descent-based optimization algorithm. This algorithm alternates between updating beamforming vectors and UAV trajectories, offering a high-quality suboptimal solution to the original problem. Theoretical analyses reveal that when the detection probability threshold is sufficiently small, a linear correlation emerges between the maximum relative variation ratio in the average received signal power at wardens under two hypotheses and the detection probability. Furthermore, to enhance covertness against the wardens, it is necessary to either decrease the projection of information beamforming covariance matrix or increase the projection of sensing beamforming covariance matrix onto the subspace spanned by the eavesdropping channel vectors. Finally, extensive simulations are presented to validate the covert performance enhancements of our proposed methodology, compared with various baseline schemes adopting existing approaches.

Unmanned aerial vehicle (UAV) communication system assisted by intelligent reflecting surface (IRS) has promising applications in future wireless communication. Considering the realistic high-efficient IRS-UAVs transmission system in the three-dimensional (3D) space, we propose a joint beamforming and UAVs’ trajectory optimization algorithm to maximize the secrecy energy efficiency (SEE) of the system, which is constrained by the mobility of UAVs and the number of access users. In the proposed algorithm, the beamforming vector, multi-IRS diagonal phase shift matrix, and UAVs’ trajectory are optimized alternately using block coordinate descent (BCD). We not only derive the optimal closed-form expression of the multi-IRS diagonal phase shift matrix, but also solve the 3D trajectory optimization of UAVs under the given beamforming matrix. Simulation results show the effectiveness of the proposed algorithm and the performance improvement for IRS-UAVs communication.

This paper proposes an integrated anti-jamming covert communication and sensing system assisted by reconfigurable intelligent surfaces (RIS). By jointly optimizing beamforming vectors and RIS phase shifts, the system maximizes the sum transmission rate while enhancing communication security, sensing accuracy, and anti-jamming capability. We present two comprehensive optimization schemes: a perfect scheme under ideal channel conditions and a robust scheme for practical scenarios. The perfect scheme jointly optimizes beamforming and phase shifts when perfect channel state information (CSI) is available, establishing a performance upper bound. The robust scheme addresses practical transmission challenges by transforming stochastic uncertainties from imperfect CSI and phase shift errors into deterministic constraints through statistical expectation analysis and worst-case formulations, ensuring reliable system performance under realistic conditions. Both schemes effectively solve the resulting non-convex problems through innovative mathematical reformulations using fractional programming, quadratic transformation techniques, and the alternating direction method of multipliers. Comprehensive simulation results demonstrate significant advantages of our proposed framework in communication reliability, sensing accuracy, and resilience against the jammer compared to conventional approaches.

Chapter 6 - Energy-efficient path planning of a solar-powered UAV for secure communication in the presence of eavesdroppers and no-fly zones

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) has been deemed a promising paradigm to provide ubiquitous communication and computing services for the Internet of Things (IoT). Besides, by intelligently reflecting the received signals, the reconfigurable intelligent surface (RIS) can significantly improve the propagation environment and further enhance the service quality of the UAV-enabled MEC. Motivated by this vision, in this paper, we consider both the amount of completed task bits and the energy consumption to maximize the energy efficiency of the RIS-assisted UAV-enabled MEC systems with non-orthogonal multiple access (NOMA) protocol, where the bit allocation, transmit power, phase shift, and UAV trajectory are jointly optimized by an iterative algorithm with a double-loop structure based on the Dinkelbach’s method and block coordinate decent (BCD) technique. Simulation results demonstrate that: 1) our proposed algorithm can achieve higher energy efficiency than baseline schemes while satisfying the task tolerance latency; 2) the energy efficiency first increases and then decreases with the increase of the mission period and the total amount of task-input bits of IoT devices; 3) the energy efficiencies of schemes with imperfect channel state information (CSI) are lower than corresponding schemes with perfect CSI, and the performance gain of NOMA over OMA diminishes under the imperfect CSI.