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 Distributed Deduplication Systems with Improved Reliability

Abstract: Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. However, there is only one copy for each file stored in cloud even if such a file is owned by a huge number of users. As a result, deduplication system improves storage utilization while reducing reliability. Furthermore, the challenge of privacy for sensitive data also arises when they are outsourced by users to cloud. Aiming to address the above security challenges, this paper makes the first attempt to formalize the notion of distributed reliable deduplication system. We propose new distributed deduplication systems with higher reliability in which the data chunks are distributed across multiple cloud servers. The security requirements of data confidentiality and tag consistency are also achieved by introducing a deterministic secret sharing scheme in distributed storage systems, instead of using convergent encryption as in previous deduplication systems. Security analysis demonstrates that our deduplication systems are secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement the proposed systems and demonstrate that the incurred overhead is very limited in realistic environments.

Delay-Minimization Routing for Heterogeneous VANETs With Machine Learning Based Mobility Prediction

Abstract: Establishing and maintaining end-to-end connections in a vehicular ad hoc network (VANET) is challenging due to the high vehicle mobility, dynamic inter-vehicle spacing, and variable vehicle density. Mobility prediction of vehicles can address the aforementioned challenge, since it can provide a better routing planning and improve overall VANET performance in terms of continuous service availability. In this paper, a centralized routing scheme with mobility prediction is proposed for VANET assisted by an artificial intelligence powered software-defined network (SDN) controller. Specifically, the SDN controller can perform accurate mobility prediction through an advanced artificial neural network technique. Then, based on the mobility prediction, the successful transmission probability and average delay of each vehicle's request under frequent network topology changes can be estimated by the roadside units (RSUs) or the base station (BS). The estimation is performed based on a stochastic urban traffic model in which the vehicle arrival follows a non-homogeneous Poisson process. The SDN controller gathers network information from RSUs and BS that are considered as the switches. Based on the global network information, the SDN controller computes optimal routing paths for switches (i.e., BS and RSU). While the source vehicle and destination vehicle are located in the coverage area of the same switch, further routing decision will be made by the RSUs or the BS independently to minimize the overall vehicular service delay. The RSUs or the BS schedule the requests of vehicles by either vehicle-to-vehicle or vehicle-to-infrastructure communication, from the source vehicle to the destination vehicle. Simulation results demonstrate that our proposed centralized routing scheme outperforms others in terms of transmission delay, and the transmission performance of our proposed routing scheme is more robust with varying vehicle velocity.

Adaptive NN output-feedback decentralized stabilization for a class of large-scale stochastic nonlinear strict-feedback systems

Abstract: In this paper, the decentralized adaptive neural network (NN) output-feedback stabilization problem is investigated for a class of large-scale stochastic nonlinear strict-feedback systems, which interact through their outputs. The nonlinear interconnections are assumed to be bounded by some unknown nonlinear functions of the system outputs. In each subsystem, only a NN is employed to compensate for all unknown upper bounding functions, which depend on its own output. Therefore, the controller design for each subsystem only need its own information and is more simplified than the existing results. It is shown that, based on the backstepping method and the technique of nonlinear observer design, the whole closed-loop system can be proved to be stable in probability by constructing an overall state-quartic and parameter-quadratic Lyapunov function. The simulation results demonstrate the effectiveness of the proposed control scheme. Copyright © 2010 John Wiley & Sons, Ltd.

Robust Cooperative Beamforming and Artificial Noise Design for Physical-Layer Secrecy in AF Multi-Antenna Multi-Relay Networks

Abstract: This paper is concerned with an optimization problem in a two-hop relay wiretap channel, wherein multiple multi-antenna relays collaboratively amplify and forward (AF) information from a single-antenna source to a single-antenna destination, and at the same time emit artificial noise (AN) to improve physical-layer information security in the presence of multiple multi-antenna eavesdroppers (or Eves). More specifically, the problem is to simultaneously optimize the AF matrices and AN covariances for secrecy rate maximization, with robustness against imperfect channel state information of Eves via a worst-case robust formulation. Such a problem is nonconvex, and we propose a polynomial-time optimization solution based on a two-level optimization approach and semidefinite relaxation (SDR). In particular, while SDR is generally an approximation technique, we prove that SDR is optimal in the specific context here. This desirable result is obtained by careful reformulation and Karush-Kuhn-Tucker optimality analysis, where, rather interestingly, AN is found to be instrumental in providing guarantee of SDR optimality. Simulation results are provided, and the results show that the proposed joint AF-AN solution can attain considerably higher achievable secrecy rates than some existing suboptimal designs.

Long-Time Coherent Integration for Weak Maneuvering Target Detection and High-Order Motion Parameter Estimation Based on Keystone Transform

Abstract: In the airborne or spaceborne radar applications, prolonging the coherent integration time is one of the effective methods to improve the radar detection ability of a weak maneuvering target, whereas the coherent integration performance may degrade due to the complex range migration (RM) and Doppler frequency migration (DFM) effects. In this paper, detection and motion parameter estimation for a weak maneuvering target with the third-order RM and DFM are considered. Firstly, Keystone transform is applied to compensate the linear range walk. Then, the matched filtering processing is performed in the range-frequency and azimuth-time domain to eliminate the residual coupling effects between range and azimuth. Finally, a well-focused image of a moving target is obtained, and three motion parameters, i.e., velocity, acceleration, and acceleration rate, are effectively estimated. In addition, as for a fast-moving target with Doppler ambiguity, two cases, i.e., target azimuth spectrum within a pulse repetition frequency (PRF) and spanning over neighboring PRF bands, are analyzed. Compared with the generalized Radon Fourier transform (GRFT), the proposed method can acquire a close integration performance but with lower computational complexity since the parameter searching dimension is reduced. Simulated processing results are provided to validate the effectiveness of the proposed method.