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.

Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective

Abstract: Mobile Edge Computing (MEC) is considered an essential future service for the implementation of 5G networks and the Internet of Things, as it is the best method of delivering computation and communication resources to mobile devices. It is based on the connection of the users to servers located on the edge of the network, which is especially relevant for real-time applications that demand minimal latency. In order to guarantee a resource-efficient MEC (which, for example, could mean improved Quality of Service for users or lower costs for service providers), it is important to consider certain aspects of the service model, such as where to offload the tasks generated by the devices, how many resources to allocate to each user (specially in the wired or wireless device-server communication) and how to handle inter-server communication. However, in the MEC scenarios with many and varied users, servers and applications, these problems are characterized by parameters with exceedingly high levels of dimensionality, resulting in too much data to be processed and complicating the task of finding efficient configurations. This will be particularly troublesome when 5G networks and Internet of Things roll out, with their massive amounts of devices. To address this concern, the best solution is to utilize Machine Learning (ML) algorithms, which enable the computer to draw conclusions and make predictions based on existing data without human supervision, leading to quick near-optimal solutions even in problems with high dimensionality. Indeed, in scenarios with too much data and too many parameters, ML algorithms are often the only feasible alternative. In this paper, a comprehensive survey on the use of ML in MEC systems is provided, offering an insight into the current progress of this research area. Furthermore, helpful guidance is supplied by pointing out which MEC challenges can be solved by ML solutions, what are the current trending algorithms in frontier ML research and how they could be used in MEC. These pieces of information should prove fundamental in encouraging future research that combines ML and MEC.

Vehicular Edge Computing and Networking: A Survey.

Abstract: As one key enabler of Intelligent Transportation System (ITS), Vehicular Ad Hoc Network (VANET) has received remarkable interest from academia and industry. The emerging vehicular applications and the exponential growing data have naturally led to the increased needs of communication, computation and storage resources, and also to strict performance requirements on response time and network bandwidth. In order to deal with these challenges, Mobile Edge Computing (MEC) is regarded as a promising solution. MEC pushes powerful computational and storage capacities from the remote cloud to the edge of networks in close proximity of vehicular users, which enables low latency and reduced bandwidth consumption. Driven by the benefits of MEC, many efforts have been devoted to integrating vehicular networks into MEC, thereby forming a novel paradigm named as Vehicular Edge Computing (VEC). In this paper, we provide a comprehensive survey of state-of-art research on VEC. First of all, we provide an overview of VEC, including the introduction, architecture, key enablers, advantages, challenges as well as several attractive application scenarios. Then, we describe several typical research topics where VEC is applied. After that, we present a careful literature review on existing research work in VEC by classification. Finally, we identify open research issues and discuss future research directions.

Image Restoration via Simultaneous Sparse Coding: Where Structured Sparsity Meets Gaussian Scale Mixture

Abstract: In image processing, sparse coding has been known to be relevant to both variational and Bayesian approaches. The regularization parameter in variational image restoration is intrinsically connected with the shape parameter of sparse coefficients' distribution in Bayesian methods. How to set those parameters in a principled yet spatially adaptive fashion turns out to be a challenging problem especially for the class of nonlocal image models. In this work, we propose a structured sparse coding framework to address this issue--more specifically, a nonlocal extension of Gaussian scale mixture (GSM) model is developed using simultaneous sparse coding (SSC) and its applications into image restoration are explored. It is shown that the variances of sparse coefficients (the field of scalar multipliers of Gaussians)--if treated as a latent variable--can be jointly estimated along with the unknown sparse coefficients via the method of alternating optimization. When applied to image restoration, our experimental results have shown that the proposed SSC---GSM technique can both preserve the sharpness of edges and suppress undesirable artifacts. Thanks to its capability of achieving a better spatial adaptation, SSC---GSM based image restoration often delivers reconstructed images with higher subjective/objective qualities than other competing approaches.

Multidirection Piezoelectricity in Mono- and Multilayered Hexagonal α-In2Se3.

Abstract: Piezoelectric materials have been widely used for sensors, actuators, electronics, and energy conversion. Two-dimensional (2D) ultrathin semiconductors, such as monolayer h-BN and MoS2 with their atom-level geometry, are currently emerging as new and attractive members of the piezoelectric family. However, their piezoelectric polarization is commonly limited to the in-plane direction of odd-number ultrathin layers, largely restricting their application in integrated nanoelectromechanical systems. Recently, theoretical calculations have predicted the existence of out-of-plane and in-plane piezoelectricity in monolayer α-In2Se3. Here, we experimentally report the coexistence of out-of-plane and in-plane piezoelectricity in monolayer to bulk α-In2Se3, attributed to their noncentrosymmetry originating from the hexagonal stacking. Specifically, the corresponding d33 piezoelectric coefficient of α-In2Se3 increases from 0.34 pm/V (monolayer) to 5.6 pm/V (bulk) without any odd–even effect. In addition, we also de...

A Circularly Polarized High-Gain Antenna With Low RCS Over a Wideband Using Chessboard Polarization Conversion Metasurfaces

Abstract: A new approach for the gain enhancement and wideband radar cross section (RCS) reduction of an antenna based on the chessboard polarization conversion metasurfaces (CPCMs) is proposed. Compared with the previous low-RCS antennas, high gain and wideband low RCS of a circularly polarized (CP) antenna are achieved simultaneously. The proposed CPCM is the chessboard configuration of the polarization conversion metasurfaces (PCMs), which is made up of adjoining two-layer substrates with three metallic patterns. Low RCS is realized by 180° (±30°) reflection phase variations between two neighboring PCMs. Gain enhancement is achieved by employing a Fabry-Perot cavity, which is constructed by the PCM and the ground of the antenna. The antenna with CPCM operating at the $X$ -band, excited by a sequentially rotated feeding network, is fabricated and measured. Simulated and measured results show that the left-hand CP gain of the antenna with CPCM is at least 3 dB higher than that of the reference antenna from 8.5 to 9.5 GHz and the monostatic RCS is effectively reduced from 6 to 14 GHz.