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.

This paper investigates the joint user and relay selection algorithm for cooperative non-orthogonal multiple access (NOMA) networks, where multiple users transmit messages to two destinations by utilizing multiple amplify-and-forward (AF) relays. To improve the outage performance of system, an optimal selection criterion is proposed at the user-relay pairs. We derive the closed-form analytical expressions on the outage probability, as well as the asymptotic expressions for large transmission power. Based on the derived analytical expressions, the effect of the number of relays and the target data rate on the outage performance are revealed. Results indicate that when the target data rate becomes small enough, the diversity order of the outage probability equals to the number of relays, while the target data rate grows without bound, the outage probability achieves one.

Baek et al. proposed a deep learning-based signal detector for conventional MIMO systems, which is a pioneering work of applying artificial intelligence into wireless communications. Although this work works well under static fading channels, it is worth notable that it fails to work under block-fading channels. In particular, the detection BER under block-fading channels is around 0.5 even in the high regime of SNR. To explain this unexpected result, we provide some simple yet efficient theoretical analysis, which clearly verifies that the proposed detector cannot decouple the phases between the channel parameters and transmitted signals and hence it fails to detect the transmitted signals under block-fading channels. The results in this paper can help understand and improve the detector. In particular, the detector structure should incorporate the channel state information for the application under block-fading channels.

In this paper, we investigate the reliability and security performance of cooperative multirelay systems, where both source and relay nodes are energy-constrained nonlinear energy harvesters, scavenging energy from a power beacon nearby. Our analysis is based on practical model since residual hardware impairments (RHIs) and channel estimation errors (CEEs) are considered. Aiming at improving the system efficiency, three representative relay selection strategies are considered: i) random relay selection (RRS); ii) suboptimal relay selection (SRS) and iii) optimal relay selection (ORS). To characterize the security performance of the considered strategies, we derive closed-form analytical expressions of the reliability and security in terms of outage probability (OP) and intercept probability (IP).We further discuss the asymptotic expressions and scaling laws of OP with the number of relays. The IP is analyzed for non-colluding and colluding scenarios. The numerical results illustrate that: i) There is a tradeoff between reliability and security, that is when the outage constraint is relaxed, the IP can be enhanced, and vice versa; ii) The outage performance of the ORS and SRS schemes outperform RRS, indicating that relay selection can enhance reliability performance; iii) There are error floors for the OP due to the CEEs; iv) Colluding eavesdroppers can enhance eavesdropping attacks by sharing their intercepted information; and v) Although RHIs and CEEs have deleterious effects on the OP, they can protect the information transmission against eavesdropping attacks.

In this paper, we investigate the design of the trajectory of an unmanned aerial vehicle (UAV) and the transmit power of ground users to improve covert communications against a flying warden in UAV-assisted non-orthogonal multiple access networks, where the legitimate UAV can simultaneously collect the messages from the multiple ground users in a secure manner. Taking the channel uncertainty into account, we derive the analytical expressions of the optimal normalized detection threshold, the minimum detection error probability (DEP), and the security-guaranteed transmit power constraint for the ground users that are exploited to ensure a high DEP at the warden UAV. Subsequently, the design to maximize the average covert achievable rate (CAR) subject to the constraints of flight speed, initial and final locations, transmit power, and detection performance is formulated as a non-convex optimization problem. To obtain a high-quality solution to the design problem at hand, an iterative block coordinate descent-based successive convex approximation method is proposed. From the theoretical analysis, we find that a greater channel estimation uncertainly or a lower effective received power ratio at the warden UAV is beneficial in enhancing the system covertness towards the warden UAV. Moreover, when the successful detection probability of the warden UAV is sufficiently small, the maximum effective received power ratio is linearly and positively correlated to the detection success probability. Besides, extensive simulations are presented to verify the covert performance advantages brought by the proposed method.

In this paper, we investigate the power control strategy of intelligent secure communication with statistic channel state information (CSI) for Internet of Things (IoT) networks, where a transceiver and an attacker with several attack types, including silent, eavesdrop, jamming and spoofing, are considered. In order to solve the security problem that the transmitter only knows the statistical CSI of attacker, we propose a power control strategy based on Q-learning. In particular, Alice and Eve can choose their actions flexibly to maximize their reward under different system state and learn their best strategy according to the proposed strategy. In addition, the interactions between Alice and Eve are formulated as a zero-sum game, the Nash equilibrium and its existence conditions are deduced. Simulation results show that the impact of statistical CSI of attacker on system security performance can be reflected by the cost of attacker to launch attack and the average channel gain parameters. More importantly, the obtained results also show that the proposed power control strategy based on statistical CSI of attacker is worse than the scheme based on instantaneous CSI for statistical CSI leads a performance loss in terms of security.