In this paper, we focus on the resource optimization issue in Wireless Body Area Networks (WBANs) section of a novel Biomedical Cyber Physical System (BM-CPS) in which the Digital Twin (DT) technique is adopted concurrently. We propose a scenario where multiple physiological sensor nodes continuously monitor the electrophysiology signals from patients and connect to a Cyber Managing Center (Cyb-MC) in a wireless way to transmit the physiological indices to the paramedic reliably. Specifically, to optimize the energy efficiency of physiological sensors while ensuring the reliability of emergency-critical electrophysiology signals transmissions and modeling the uncertain stochastic environments, the optimization issue is transformed into a Markov Decision Process (MDP) in terms of joint transmission mode, relay assignment, transmission power and time slot scheduling consideration. Subsequently, we propose a Random Graph-enabled Deep Deterministic Policy Gradient (RG-DDPG) scheme to tackle the challenge while releasing computing complexity. In addition, as an innovative paradigm for reshaping cyberspace applications, the DT of WBAN is created to capture the time-varying resource status, where virtualized and intelligent resource management can be performed uniformly. Finally, extensive simulation studies verified the advancement of the proposed scheme and demonstrated that the DT can mirror the topology, predict the behavior, and administrate the resources of the WBANs.

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.

The semantic segmentation of remote sensing images is extensively used in crop cover and type analysis and environmental monitoring. In the semantic segmentation of remote sensing images, owning to the specificity of remote sensing images, not only the local context is required, but also the global context information makes an important role in it. Inspired by the powerful global modeling capability of Swin Transformer, we propose the Local and Spatial Enhancement Net (LSENet) network, which follows the encoder–decoder architecture of the UNet network. In the encoding phase, we propose spatial enhancement module (SEM), which helps Swin Transformer further enhance feature extraction by encoding spatial information. In the decoding stage, we propose local enhancement module (LEM), which is embedded in the Swin Transformer to improve the Swin Transformer to assist the network to obtain more local semantic information so as to classify pixels more accurately, especially in the edge region, the adding of LEM enables to obtain smoother edges. The experimental results on the Vaihingen and Potsdam datasets demonstrate the effectiveness of our proposed method. Specifically, the mIoU metric is 78.58% on the Potsdam dataset and 72.59% on the Vaihingen dataset.

By offloading some computational tasks to the edge server, edge computing can help relieve the increasing computation burden of mobile users and improve the quality of experience of users. In this article, we investigate the computation offloading in edge computing-enabled multiuser cell-free (CF) multi-input multioutput (MIMO) networks with multiple building baseband units (BBUs). The user association, transmit covariances, CPU-cycle frequencies, and allocated bandwidths are jointly optimized to minimize two objectives: 1) the energy consumption of mobile devices (MDs) and 2) execution delay of tasks. We formulate it as a vector optimization problem and adopt the scalarization technique to transform it into a scalar mixed-integer nonlinear programming (MINLP). Then, to solve the MINLP, we introduce user sorting into the framework of branch and bound and propose a sorted MD association (MDA) method. Two key issues are tackled in the proposed MDA, i.e., the lower bound of the MINLP and the updation of incumbent solution. For the first issue, we present a Lagrangian dual relaxation algorithm based on subgradient projection, while the second one involving another nonconvex minimization problem, we propose a successive convex approximation (SCA)-based algorithm to solve it. Finally, extensive simulations demonstrate that the proposed method is able to reduce the system cost significantly and achieve better system performance by comparing with the benchmark schemes.

The incorporation of intelligent reflecting surface (IRS) into wireless communication systems can extend the coverage and enhance the data transmission rate. This paper studies the joint transceiver and IRS designs in IRS-assisted multi-input multi-output (MIMO) systems under both perfect channel state information (CSI) and imperfect CSI. Specifically, the transmit precoder, reflection coefficients at the IRS, and receive equalizer are jointly optimized to minimize the data detection mean square error (MSE), subject to the transmission power constraint and the modulus constraints for IRS reflection coefficients. The design problems, non-convex and challenging, are tackled under the framework of alternating optimization. For the design with perfect CSI, we successively optimize the IRS reflection coefficients given the precoder and present the closed-form optimal angle of one reflection coefficient given the others. For the robust design with imperfect CSI, we first average the detection MSE over channel uncertainties by using a generalized statistical CSI error model. Then, the averaged MSE is approximated by a more tractable upper bound. Subsequently, the robust design problem is elaborately transformed into a form similar to the problem with perfect CSI. Numerical results demonstrate the effectiveness of the proposed designs as compared to various benchmark schemes.

We consider the optimal precoder design with the assumption that the transmitter only has channel covariance information, for the multi‐input multi‐output (MIMO) information and energy transmission system. The objective of the system design is to maximize the average system information rate, meanwhile meeting the minimum energy requirement of the energy receiver. Following this objective, we formulate the problem as a semidefinite programming (SDP) and further transform it into a dual problem. Two methods are proposed to solve this problem: the first method decomposes the transmission covariance as a product of precoders so that the constrained optimization becomes an unconstrained one, whereas the second method derives the structure of the optimal transmission covariance analytically. Both methods are proved to be convergent and their overheads and complexity are also analyzed. The achievable rate‐energy (R‐E) regions for the proposed methods are presented in the simulation. Under various system settings, the superiority of the proposed methods is shown by comparing with a few existing transmission schemes.

This paper investigates the effect of untrusted relay on the secrecy outage probability of amplify- and-forward relay networks with the help of direct links and multiple users. We consider three selection criteria which are based on the direct and relaying link. The impacts of both the direct and relaying links on SOP performance is studied by deriving the closed-form analytical SOP as well as the asymptotic expressions. Simulation results are provided to confirm the theoretical analysis. It can be found that the diversity order of optimal selection criterion is equal to the number of users, while the diversity order of the sub-optimal criterion based on relaying links is equal to one.

This paper studies user selection for an untrusted decode-and-forward relay channel, in which one base station is selected for secure transmission to the destination node in the presence of direct links. To enhance the secrecy performance, three selection criteria are investigated based on the the direct and the relaying links. The close-form analytical secrecy outage probability (SOP) as well as the asymptotic expressions are derived to reveal the impact of the direct and the relaying link on the system secrecy performance. From the asymptotic expressions, it shows that the proposed sub-optimal criterion, which substantially reduces the system complexity, achieves the same diversity order with the optimal criterion.

This paper investigates the secure communications of multiuser untrusted amplify-and-forward relay networks in the presence of direct links. In the considered system, one user is selected among multiple ones for secure transmission to the destination node with the help of an untrusted relay node. To reduce the information leakage to the untrusted relay, the paper considers two selection criteria to select the user based on the direct and the relaying links, respectively. The impact of direct and the relaying link on the system secrecy performance is studied by deriving the close-form ergodic secrecy rate (ESR) as well as the asymptotic expression. From the asymptotic expression, it can be found that the asymptotic ESR increases linearly with the logarithm of the average channel gain ratio of the direct link to the relaying link.

The performance of the vector orthogonal frequency division multiplexing (V‐OFDM) system over fast fading channels is investigated. The channel is time varying within one V‐OFDM data‐block period, which causes the inter‐carrier interference (ICI). With the help of an equivalent V‐OFDM system model and an auxiliary transform matrix, a novel mathematical expression for the received signal with the ICI signal is derived, which clearly expresses how the signal of a target vector block (VB) is interfered by other VBs. The ICI signal is analysed and two theorems about its property are proposed. The first theorem presents the expression of the power of the ICI signal, showing that the ICI signal power increases with the VB number. The other one indicates that ICI signals at different subcarriers are not correlated. With the two theorems, a novel detection method utilising the correlation of the ICI signal is therefore proposed. Numerical results verify the validity of the derived theorems and simulation results demonstrate that the proposed minimum mean square error (MMSE) detection can suppress the ICI effect and it is superior to the conventional MMSE detection in terms of both detection mean square error and bit error rate performance.