For the sake of enhancing the covertness and sensing performance in the integrated sensing and covert communications (ISCCs) system, we design a beamforming framework for reconfigurable intelligence surface (RIS) assisted ISCC. Specifically, the system intends to transmit information to a legitimate receiver (Bob) covertly and sense the target simultaneously while avoiding being detected by a warden (Willie). RIS can be applied to both traditional single-connected networks and a broad fully connected networks. By jointly optimizing the beamforming vector of the communication and the autocorrelation matrix of the sensing, and the phase shift matrix of the RIS, both the convert rate and the target’s probing power are maximized. And the covertness and the constant-mode constraints are considered. A multistrategy alternate optimization (MSAO) algorithm is proposed to solve the optimization problem based on quadratic constraint quadratic programming (QCQP) and semidefinite relaxation (SDR). Furthermore, we consider a more realistic application scenario where the legal party has imperfect channel state information of Willie. Simulation results show that deploying RIS in a generalized fully connected mode can achieve better transmission of beampatterns and increase upper limit of covert communications rate than conventional single-connected mode.

This article proposes an energy-efficient covert offloading scheme for blockchain-enabled Internet of Things (IoT), allowing sensors to upload tasks undetected by adversaries while ensuring satisfaction in paid computation offloading. Covert communication conceals the existence of transmitted signals or links. However, existing schemes primarily rely on artificial noise (AN) or wireless channel uncertainty, resulting in low covert rates for IoT offloading scenarios. Additionally, blockchain-enabled IoT, being value-oriented, necessitates consideration of sensors’ satisfaction during covert offloading. To tackle these challenges, the proposed scheme combines the adversary’s channel estimation errors with AN to enhance the covert rate, while also matching sensors’ satisfaction with the computation resources of mobile edge servers. Notably, a closed-form expression of the average minimum error detection probability is derived to maximize the effective covert rate. Furthermore, an integrated algorithm combining the Kuhn-Munkres (KM) algorithm with two bubble sort algorithms is designed to minimize energy consumption. Both analytical and simulation results demonstrate that the proposed scheme significantly reduces energy consumption compared to existing solutions.

The physical layer security of downlink nonorthogonal multiple access (NOMA) network is analyzed. In order to improve the secrecy probability, friendly jammers are jointed in the NOMA network. Two jammer schemes are proposed in the NOMA network. All the jammers transmit jamming signal without jammer selection in the first scheme (NO JS scheme). Jammers are selected to transmit jamming signal if their interfering power on scheduled users is below a threshold in the second scheme (JS scheme). A stochastic geometry approach is applied to analyze the outage probability and the secrecy probability. Compared with the NO JS scheme and traditional scheme (without jointing jammers), the jammer selection scheme provides a good balance between the user outage probability and secrecy probability. Numerical results demonstrate that the security performance of the two proposed schemes can be improved by jointing the jammers in the NOMA wireless network.

A near-field based on a semi-passive intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) framework is proposed. Our objective is to identify the optimal beam direction from the base station to both the communicating user and the sensing target through downlink beam scanning signals. Especially, in the communication module, the beam scanning process in the downlink by IRS’s passive reflecting elements (REs) searches for the optimal reflected beam and sends the signal from the base station to the communicating user. Meanwhile, in the sensing module, the IRS can estimate the location of nearby targets by analyzing the echo signals received by the active sensing elements (SEs) on the IRS. To achieve the above objectives, in this paper, we design the near-field codebook by considering the near-field cascaded array steering vectors of the IRS and match the optimal codewords of the IRS through an exhaustive training process, so as to efficiently complete the near-field beam training under the matched near-field channel model. We have done a performance analysis of this ISAC system for the Cramer-Rao bound (CRB), and CRB provides performance constraints for near-field target sensing. Specifically, the CRB curve shows a monotonic decrease with increasing beam scan duration, while the achievable rate initially increases and then decreases. Therefore, we need to set the size of the model codebook space reasonably to balance the perception and communication performance. Simulation results show that the perceptual performance of our proposed scheme in the ISAC system is close to the derived CRB.

In movable antennas (MAs) systems, implementing channel estimation remains a significant challenge due to pilot overhead and sparsity issues. To address these issues, we study a 5-D tensor-based Bayesian factorization for angles of departure (AOD) and angles of arrival (AOA) estimation in wireless communications with MAs in this paper. The MAs are utilized to reconstruct the channel response at different antenna positions, and we propose a algorithm that utilizes constructed received signals to reconstruct a fifth-order complex tensor model. To reduce computational complexity for angle estimation, the array structure properties are used to transform the tensor to a real-valued tensor model. Finally, we derive the Bayesian fifth-order tensor factorization from the constructed real-valued model and extract angular information using the approximated factor matrices obtained through the decomposition. We also derive the Cramér-Rao Bound (CRB) for angle estimation, where the angle parameters to be estimated rely on a finite set of channel measurements collected at random locations by the MA at both the transmitter and receiver. Our proposed method offers higher estimation accuracy compared to other angle estimation methods and is also effective when the number of transmission paths is unknown. The simulation results confirm the stability and effectiveness of the proposed angle estimation method based on Bayesian inference.