Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Computer science. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

Secure MISO-NOMA Transmission With Artificial Noise

Abstract: This correspondence studies a new secrecy beamforming (SBF) scheme for multiple-input single-output non-orthogonal multiple access (MISO-NOMA) systems. In particular, the proposed SBF scheme efficiently exploits artificial noise to protect the confidential information of two NOMA assisted legitimate users, such that only the eavesdropper's channel is degraded. Considering a practical assumption of the imperfect worst-case successive interference cancellation which is a unique character in employing NOMA transmission, we derive a closed-form expression for the secrecy outage probability to characterize the secrecy performance. After that, we carry out an analysis of secrecy diversity order to provide further insights about secure MISO-NOMA transmission. Numerical results are provided to demonstrate the accuracy of the developed analytical results and the effectiveness of the proposed SBF scheme.

Bipartite Consensus in Networks of Agents With Antagonistic Interactions and Quantization

Abstract: This brief deals with the consensus problem in a network of agents with cooperative and antagonistic interactions subject to quantization. By employing the techniques from nonsmooth analysis, we prove that all agents can be guaranteed to asymptotically reach bipartite consensus for any logarithmic quantizer accuracy under connected and structurally balanced topology and the states of all agents asymptotically converge to zero under connected and structurally unbalanced topology. In addition, finite-time bipartite consensus is considered for single-integrator agents with binary quantized information. The simulation results are given to demonstrate the effectiveness of the theoretical results.

Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning

Abstract: Cross-domain visual data matching is one of the fundamental problems in many real-world vision tasks, e.g., matching persons across ID photos and surveillance videos. Conventional approaches to this problem usually involves two steps: i) projecting samples from different domains into a common space, and ii) computing (dis-)similarity in this space based on a certain distance. In this paper, we present a novel pairwise similarity measure that advances existing models by i) expanding traditional linear projections into affine transformations and ii) fusing affine Mahalanobis distance and Cosine similarity by a data-driven combination. Moreover, we unify our similarity measure with feature representation learning via deep convolutional neural networks. Specifically, we incorporate the similarity measure matrix into the deep architecture, enabling an end-to-end way of model optimization. We extensively evaluate our generalized similarity model in several challenging cross-domain matching tasks: person re-identification under different views and face verification over different modalities (i.e., faces from still images and videos, older and younger faces, and sketch and photo portraits). The experimental results demonstrate superior performance of our model over other state-of-the-art methods.

Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization

Abstract: In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) with the objective to optimize computation offloading with minimum UAV energy consumption. In the considered scenario, a UAV plays the role of an aerial cloudlet to collect and process the computation tasks offloaded by ground users. Given the service requirements of users, we aim to maximize UAV energy efficiency by jointly optimizing the UAV trajectory, the user transmit power, and computation load allocation. The resulting optimization problem corresponds to nonconvex fractional programming, and the Dinkelbach algorithm and the successive convex approximation (SCA) technique are adopted to solve it. Furthermore, we decompose the problem into multiple subproblems for distributed and parallel problem solving. To cope with the case when the knowledge of user mobility is limited, we adopt a spatial distribution estimation technique to predict the location of ground users so that the proposed approach can still be applied. Simulation results demonstrate the effectiveness of the proposed approach for maximizing the energy efficiency of UAV.