This work investigates joint covert and secure communications via intelligent reflecting surface (IRS) in a non-line-of-sight (NLOS) wireless network in the presence of an eavesdropper and a warden. We consider a coor...
This work investigates joint covert and secure communications via intelligent reflecting surface (IRS) in a non-line-of-sight (NLOS) wireless network in the presence of an eavesdropper and a warden. We consider a coordinator of Ben communicates with two users, where one user requires secure communication and the other seeks covert communication. An optimization problem is proposed to maximize the covert rate subject to the constraints of covert requirement, secure communication rate, amplitudes and phase shifts of reflecting elements, and transmit powers of secure and covert communications. We use the successive convex approximation(SCA) method to solve this problem. The numerical results illustrate the effectiveness of utilizing an IRS and highlight the impact of the number of reflecting elements on enhancing communication quality.
Current single image derain methods cannot solve the heavy rain situation well. In this paper, based on the physical model of a rainy image, we build a two-stage network, TSF-Net, which combines model-driven and data-...
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Traffic prediction is of vital importance in intelligent transportation systems. It can realize efficient route planning, avoid traffic congestion and reduce travel time, etc. However, it is difficult to make accurate...
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Traffic prediction is of vital importance in intelligent transportation systems. It can realize efficient route planning, avoid traffic congestion and reduce travel time, etc. However, it is difficult to make accurate road traffic prediction, due to the complex spatiotemporal dependencies of the traffic network. The construction and learning of spatial dependencies play a pivotal role in road traffic prediction, laying the foundation for accurate traffic prediction. Regrettably, most of the existing methods for capturing spatial dependencies only consider a single spatial relationship, ignoring other potential temporal and spatial correlations in the traffic network. Moreover, the end-to-end training methods are difficult to control the training direction during graph learning. Additionally, the result of traffic forecasting lacks of fusing multiple traffic data information. All of these have adverse effects on road traffic prediction. In other to capture the spatiotemporal dependencies of the traffic network accurately, a novel traffic prediction framework, Adaptive Spatio-Temporal Graph Neural Network based on Multi-graph Fusion (DTS-adapSTNet), is proposed. First of all, in order to better extract the hidden spatial dependencies, a method of fusing multiple factors is designed, which includs the distance relationship, transfer relationship and same-road segment relationship of traffic data, etc. Secondly, an adaptive learning method is proposed, which can control the learning direction of parameters better by the adaptive matrix generation module and traffic prediction module. Thirdly, an improved loss function is designed for training processes and a multi-matrix fusion module is designed to perform weighted fusion of the learned matrices, updating the spatial adjacency matrix continuously, which fuses as much traffic information as possible for more accurate traffic prediction. Finally, based on the experiments by two large real-world datasets, the DTS-adapSTNe
Federated linear regressions have been developed and applied in various domains, where multiparties collaboratively and securely perform optimization algorithms, e.g., Gradient Descent, to learn a set of optimal model...
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Pose estimation is important for robotic perception, path planning, etc. Robot poses can be modeled on matrix Lie groups and are usually estimated via filter-based methods. In this paper, we establish the closed-form ...
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In this paper, we study the statistical properties of distributed kernel ridge regression together with random features (DKRR-RF), and obtain optimal generalization bounds under the basic setting, which can substantia...
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Interference and scattering, often deemed undesirable, are inevitable in wireless communications, especially when the current mobile networks and upcoming sixth generation (6G) have turned into ultra-dense networks. C...
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The miniaturization and integration of beam steering devices have consistently been the focus of the field. Conventional methods alter the eigenmode of the optical cavity by regulating the refractive index. Due to the...
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Channel modeling is indispensable in a communication system. In this paper, a novel scheme for channel modeling using quantum generative adversarial model was proposed. A quantum generative adversarial network is a ge...
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ISBN:
(纸本)9781665450867
Channel modeling is indispensable in a communication system. In this paper, a novel scheme for channel modeling using quantum generative adversarial model was proposed. A quantum generative adversarial network is a generative adversarial model with a quantum circuit as the generative module and a deep neural network as the discriminant module, thereby exploiting the privilege of quantum algorithms in simulating probability distributions to stochastic channel models. Experiments were conducted on IBM QX quantum computing platform. The gradient descent of the cost function and Kullback-Leibler divergence were analyzed. Results verify the feasibility and superiority of the quantum generative adversarial network for channel modeling.
Communication efficiency is one of the key bottlenecks in Federated Learning (FL). Compression techniques, such as sparsification and quantization, are used to reduce communication overhead. However, joint designs of ...
Communication efficiency is one of the key bottlenecks in Federated Learning (FL). Compression techniques, such as sparsification and quantization, are used to reduce communication overhead. However, joint designs of these techniques under communication constraints are not well-explored. This paper investigates the joint uplink compression problem in communication-constrained FL systems. We propose a Block-TopK sparsification scheme to reduce the proportion of bits used for locating entries of a sparsified vector. Considering the communication constraints, an optimization formulation is proposed to minimize the compression error. By solving the optimization problem, our joint compression method provides a better trade-off between sparsity budget and bit width. Numerical results demonstrate that our approach achieves 99.96% of baseline accuracy with only 1.56% of the baseline communication overhead when training ResNet-18 on CIFAR-10.
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