In this paper, we develop an energy minimization framework for wireless-powered multi-access edge computing (WP-MEC) networks, where two zero-energy reconfigurable intelligent surfaces (zeRISs) are employed to support energy harvesting and task offloading. In the downlink energy harvesting period, the hybrid access point (HAP) provides energy beamforming for multiple zero-energy devices and two zeRISs, where the harvested energy is used to offload tasks in the uplink period. Specifically, an optimization problem is formulated to minimize the energy consumption at the HAP, jointly considering the nonlinear energy harvesting model and cascaded RIS link. Next, an efficient iterative solution is designed to realize joint time allocation, HAP energy beamforming, and reflection coefficients for two-RIS by employing alternating optimization and semidefinite relaxation methods. Numerical results demonstrate the superiority of the algorithm. Compared to the corresponding single RIS setup, the energy consumption of HAP under the proposed two-zeRIS scheme is significantly reduced.

In this work, a novel double-covertness framework is developed for integrated sensing and communication (ISAC) systems, where both the radar and communication signals are protected from being maliciously detected by adversaries. Specifically, a new measurement named joint intercept probability (JIP) is proposed for characterizing the double-covertness performance. Next, an optimal power allocation strategy is designed to minimize the JIP, subject to certain quality-of-service (QoS) requirements for communication and sensing. Simulation results verify the effectiveness of our proposed solution and demonstrate the intrinsic relationship among power allocation, JIP and different QoS requirements.

In this paper, a novel artificial noise (AN)-aided secure and covert integrated sensing and communication (ISAC) framework is established for uncrewed aerial vehicle (UAV) systems, to against malicious radar signal detection and communication eavesdropping. Specifically, we consider that besides the communication and sensing signals, the AN signal, which is used to interfere with the eavesdropper and conceal the existence of radar signal, will be transmitted by the UAV-enabled base station (UBS) with uncertainty on its power level. The closed-form expressions of intercept probability (IP) as well as the minimum detection error probability (M-DEP) are derived. Moreover, an efficient communication and sensing performance maximization strategy is designed by optimizing the beamforming vector of communication, covariance matrix of sensing, and UBS receiver filter jointly, to satisfy the IP, power and M-DEP constraints. Simulation results are provided to verify the effectiveness of our joint design by comparing it to benchmark strategy. Moreover, the impact of AN power uncertainty is examined via simulations.

In this paper, aimed to against spoofing attack, we propose a novel physical layer authentication (PLA) framework for unmanned aerial vehicle (UAV) communication networks under Rayleigh and Rician channels. A new PLA metric, called authentication distance (AD), is defined by jointly considering the geographical locations, elevation angles, and channel randomness between a legitimate sensor and a malicious spoofer. For Rayleigh channel in dense urban environment, the closed-form expressions for the false alarm probability (FAP) and miss detection probability (MDP) are obtained by adopting method of convolution and integration by parts. Next, the PLA hypothesis test model with Rician channel is established in suburban environment where both the Rician factor and the path loss exponent are functions of UAV altitude. To proceed, the expressions for the FAP and MDP are derived based on the doubly non-centralFdistribution. In addition, MDP minimization solutions subject to certain FAP requirement are developed in both Rayleigh and Rician channels by optimizing the detection threshold and UAV altitude jointly. Simulation results show that our derived analytical expressions of FAP and MDP match the Monte Carlo simulations well. Moreover, simulation results also imply the effectiveness of the proposed PLA framework for UAV communication networks.

The utilization of Specific Emitter Identification (SEI) in war defense has significantly improved the capability to identify and analyze enemy targets through the use of radio frequency fingerprint (RFF) extracted from received signals to identify specific emitters. The application of deep learning (DL) technology, specifically in wireless security authentication, has the potential to further enhance SEI. However, traditional machine learning approaches are centralized-based, which is not optimal for SEI due to the private nature of the emitter dataset, especially when the dataset are distributed among different organizations. Federated learning (FL) offers a solution by allowing multiple clients to cooperate in model training without dataset exchange. This paper introduces FL into SEI and proposes an open-set recognition framework. Unlike closed-set recognition, which only recognizes the emitters in the training set, open-set recognition is capable of identifying emitters that are not in the training set. Therefore the proposed framework is extremely suitable for the unknown emitter identification. The experimental results show that the proposed scheme achieves a high level of accuracy in identifying unknown emitters, even if the model is trained distributedly.

In this paper, we develop a secure multi-layer mobile edge computing (MEC) system where an unmanned aerial vehicle (UAV) equipped with a reconfigurable intelligent surface (RIS) acts as an aerial edge server and assists the offloading from multiple ground users to a base station (BS), in the presence of a full-duplex active eavesdropper (AE). To enhance the computing performance, we consider a partially offloading scheme where the computational task at each user can be executed at itself and offloaded to the UAV edge server and the BS via the UAV-enabled RIS, respectively. To maximize the total number of secure computing tasks among all users, we design a low complexity iterative algorithm by jointly optimizing the RIS phase shift, UAV deployment, power and computing resource allocation subject to certain power constraints. Numerical results show that compared to benchmark offloading schemes, our proposed UAV-RIS aided multi-layer MEC design improves the computing performance by at least 12.91%. Numerical results also demonstrate the impact of the full-duplex AE and validate the robustness of our proposed solution.

In this paper, we present a novel unmanned aerial vehicle (UAV) aided physical layer authentication (PLA) frame-work to detect the origin of the received signal between a legitimate transmitter and a malicious adversary, based on the physical properties of channel characteristics and geographical locations. First, we model the authentication hypothesis test at the UAV based on the signal-to-noise ratio (SNR) of each transmission and analyze the probability density functions (PDFs) of SNR differences. Then, we derive the explicit expressions of false alarm probability (FAP) and miss detection probability (MDP), both of which depict the occurrence of detection error. Next, with the aim of minimizing the MDP subject to a given FAP constraint, the detection threshold and UAV deployment are jointly optimized. Numerical results verify the accuracy of our derived expressions and demonstrate the impact of distribution rate and adversary’s location on the detection performance. Moreover, numerical results also highlight the superiority of our proposed solution using SNR differences over benchmark strategy in high-rise urban environment.

In this paper, we propose a low-latency virtual reality (VR) delivery system where an unmanned aerial vehicle (UAV) base station (U-BS) is deployed to deliver VR content from a cloud server to multiple ground VR users. Each VR input data requested by the VR users can be either projected at the U-BS before transmission or processed locally at each user. Popular VR input data is cached at the U-BS to further reduce backhaul latency from the cloud server. For this system, we design a low-complexity iterative algorithm to minimize the maximum communications and computing latency among all VR users subject to the computing, caching and transmit power constraints, which is guaranteed to converge. Numerical results indicate that our proposed algorithm can achieve a lower latency compared to other benchmark schemes. Moreover, we observe that the maximum latency mainly comes from communication latency when the bandwidth resource is limited, while it is dominated by computing latency when computing capacity is low. In addition, we find that caching is helpful to reduce latency.

In this paper, we propose a secure unmanned aerial vehicle (UAV) mobile edge computing (MEC) system where multiple ground users offload large computing tasks to a nearby legitimate UAV in the presence of multiple eavesdropping UAVs with imperfect locations. To enhance security, jamming signals are transmitted from both the full-duplex legitimate UAV and non-offloading ground users. For this system, we design a low-complexity iterative algorithm to maximize the minimum secrecy capacity subject to latency, minimum offloading and total power constraints. Specifically, we jointly optimize the UAV location, users' transmit power, UAV jamming power, offloading ratio, UAV computing capacity, and offloading user association. Numerical results show that our proposed algorithm significantly outperforms baseline strategies over a wide range of UAV self-interference (SI) efficiencies, locations and packet sizes of ground users. Furthermore, we show that there exists a fundamental tradeoff between the security and latency of UAV-enabled MEC systems which depends on the UAV SI efficiency and total UAV power constraints.