Showing papers by "Beijing University of Posts and Telecommunications published in 2016"

Fog-computing-based radio access networks: issues and challenges

Abstract: An F-RAN is presented in this article as a promising paradigm for the fifth generation wireless communication system to provide high spectral and energy efficiency. The core idea is to take full advantage of local radio signal processing, cooperative radio resource management, and distributed storing capabilities in edge devices, which can decrease the heavy burden on fronthaul and avoid large-scale radio signal processing in the centralized baseband unit pool. This article comprehensively presents the system architecture and key techniques of F-RANs. In particular, key techniques and their corresponding solutions, including transmission mode selection and interference suppression, are discussed. Open issues in terms of edge caching, software-defined networking, and network function virtualization are also identified.

A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance

Abstract: While re-identification (Re-Id) of persons has attracted intensive attention, vehicle, which is a significant object class in urban video surveillance, is often overlooked by vision community. Most existing methods for vehicle Re-Id only achieve limited performance, as they predominantly focus on the generic appearance of vehicle while neglecting some unique identities of vehicle (e.g., license plate). In this paper, we propose a novel deep learning-based approach to PROgressive Vehicle re-ID, called “PROVID”. Our approach treats vehicle Re-Id as two specific progressive search processes: coarse-to-fine search in the feature space, and near-to-distant search in the real world surveillance environment. The first search process employs the appearance attributes of vehicle for a coarse filtering, and then exploits the Siamese Neural Network for license plate verification to accurately identify vehicles. The near-to-distant search process retrieves vehicles in a manner like human beings, by searching from near to faraway cameras and from close to distant time. Moreover, to facilitate progressive vehicle Re-Id research, we collect to-date the largest dataset named VeRi-776 from large-scale urban surveillance videos, which contains not only massive vehicles with diverse attributes and high recurrence rate, but also sufficient license plates and spatiotemporal labels. A comprehensive evaluation on the VeRi-776 shows that our approach outperforms the state-of-the-art methods by 9.28 % improvements in term of mAP.

Large-scale vehicle re-identification in urban surveillance videos

Abstract: Vehicle, as a significant object class in urban surveillance, attracts massive focuses in computer vision field, such as detection, tracking, and classification. Among them, vehicle re-identification (Re-Id) is an important yet frontier topic, which not only faces the challenges of enormous intra-class and subtle inter-class differences of vehicles in multicameras, but also suffers from the complicated environments in urban surveillance scenarios. Besides, the existing vehicle related datasets all neglect the requirements of vehicle Re-Id: 1) massive vehicles captured in real-world traffic environment; and 2) applicable recurrence rate to give cross-camera vehicle search for vehicle Re-Id. To facilitate vehicle Re-Id research, we propose a large-scale benchmark dataset for vehicle Re-Id in the real-world urban surveillance scenario, named “VeRi”. It contains over 40,000 bounding boxes of 619 vehicles captured by 20 cameras in unconstrained traffic scene. Moreover, each vehicle is captured by 2∼18 cameras in different viewpoints, illuminations, and resolutions to provide high recurrence rate for vehicle Re-Id. Finally, we evaluate six competitive vehicle Re-Id methods on VeRi and propose a baseline which combines the color, texture, and highlevel semantic information extracted by deep neural network.

LTE-V: A TD-LTE-Based V2X Solution for Future Vehicular Network

Abstract: Diverse applications in vehicular network present specific requirements and challenges on wireless access technology. Although considered as the first standard, IEEE 802.11p shows the obvious drawbacks and is still in the field-trial stage. In this paper, we propose long-term evolution (LTE)-V as a systematic and integrated V2X solution based on time-division LTE (TD-LTE) 4G. LTE-V includes two modes: 1) LTE-V-direct and 2) LTE-V-cell. Comparing to IEEE 802.11p, LTE-V-direct is a new decentralized architecture which modifies TD-LTE physical layer and try to keep commonality as possible to provide short range direct communication, low latency, and high reliability improvements. By leveraging the centralized architecture with native features of TD-LTE, LTE-V-cell optimizes radio resource management for better supporting V2I. LTE-V-direct and LTE-V-cell coordinate with each other to provide an integrated V2X solution. Performance simulations based on sufficient scenarios and the prototype system with typical cases are presented. Finally, future works of LTE-V are envisioned.

Blind Image Quality Assessment Based on High Order Statistics Aggregation

Abstract: Blind image quality assessment (BIQA) research aims to develop a perceptual model to evaluate the quality of distorted images automatically and accurately without access to the non-distorted reference images. The state-of-the-art general purpose BIQA methods can be classified into two categories according to the types of features used. The first includes handcrafted features which rely on the statistical regularities of natural images. These, however, are not suitable for images containing text and artificial graphics. The second includes learning-based features which invariably require large codebook or supervised codebook updating procedures to obtain satisfactory performance. These are time-consuming and not applicable in practice. In this paper, we propose a novel general purpose BIQA method based on high order statistics aggregation (HOSA), requiring only a small codebook. HOSA consists of three steps. First, local normalized image patches are extracted as local features through a regular grid, and a codebook containing 100 codewords is constructed by K-means clustering. In addition to the mean of each cluster, the diagonal covariance and coskewness (i.e., dimension-wise variance and skewness) of clusters are also calculated. Second, each local feature is softly assigned to several nearest clusters and the differences of high order statistics (mean, variance and skewness) between local features and corresponding clusters are softly aggregated to build the global quality aware image representation. Finally, support vector regression is adopted to learn the mapping between perceptual features and subjective opinion scores. The proposed method has been extensively evaluated on ten image databases with both simulated and realistic image distortions, and shows highly competitive performance to the state-of-the-art BIQA methods.

An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare

Abstract: Public healthcare has been paid an increasing attention given the exponential growth human population and medical expenses It is well known that an effective health monitoring system can detect abnormalities of health conditions in time and make diagnoses according to the gleaned data As a vital approach to diagnose heart diseases, ECG monitoring is widely studied and applied However, nearly all existing portable ECG monitoring systems cannot work without a mobile application, which is responsible for data collection and display In this paper, we propose a new method for ECG monitoring based on Internet-of-Things (IoT) techniques ECG data are gathered using a wearable monitoring node and are transmitted directly to the IoT cloud using Wi-Fi Both the HTTP and MQTT protocols are employed in the IoT cloud in order to provide visual and timely ECG data to users Nearly all smart terminals with a web browser can acquire ECG data conveniently, which has greatly alleviated the cross-platform issue Experiments are carried out on healthy volunteers in order to verify the reliability of the entire system Experimental results reveal that the proposed system is reliable in collecting and displaying real-time ECG data, which can aid in the primary diagnosis of certain heart diseases

Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

Abstract: As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including system architectures, key techniques, and open issues. The system architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues, and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, social-aware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test is introduced as well.

Uplink Nonorthogonal Multiple Access in 5G Systems

Abstract: In order to achieve diverse arrived power in uplink nonorthogonal multiple access (NOMA), an uplink power control scheme is proposed. The proposed scheme makes evolved NodeB (eNB) distinguish the multiplexing user equipments (UEs) in power domain. The outage performance and the achievable sum data rate for the proposed scheme are theoretically analyzed, and the closed-form expressions of outage probability and achievable sum data rate are derived. By simulation, we show that the outage performances are related to both the power backoff step and the target data rate. Compared with the traditional orthogonal multiple access (OMA) scheme, the proposed NOMA scheme significantly improves the achievable sum data rate.

User-centric ultra-dense networks for 5G: challenges, methodologies, and directions

Abstract: Ultra-dense networking (UDN) is considered as a promising technology for 5G. In this article, we define user-centric UDN (UUDN) by introducing the philosophy of the network serving user and the "de-cellular" method. Based on the analysis of challenges and requirements of UUDN, a new architecture is presented that breaks through the traditional cellular architecture of the network controlling user. Dynamic AP grouping is proposed as the core function of UUDN, through which a user could enjoy satisfactory and secure service following her movement. Furthermore, we provide methods for mobility management, resource management, interference management, and security issues. We point out that these functions should be co-designed and jointly optimized in order to improve the system throughput with higher resource utilization, better user experience, and increased energy efficiency. Finally, future works in UUDN are discussed.

Machine-Washable Textile Triboelectric Nanogenerators for Effective Human Respiratory Monitoring through Loom Weaving of Metallic Yarns.

Abstract: Textile triboelectric nanogenerators for human respiratory monitoring with machine washability are developed through loom weaving of Cu-PET and PI-Cu-PET yarns. Triboelectric charges are generated at the yarn crisscross intersections to achieve a maximum short circuit current density of 15.50 mA m-2 . By integrating into a chest strap, human respiratory rate and depth can be monitored.

5G 3GPP-Like Channel Models for Outdoor Urban Microcellular and Macrocellular Environments

Abstract: For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.

Enhancing the Brightness of Quantum Dot Light-Emitting Diodes by Multilayer Heterostructures

Abstract: Quantum dots (QDs) show great promise for use in nanotechnology, owing to their high quantum efficiency, color tenability, narrow emission, and high luminescence efficiency. As a new generation of light-emitting devices (LEDs), QD-LEDs have attracted a great deal of attention in displays and lighting. To meet the commercial requirements, the brightness of QD-LEDs needs to be further improved. In this work, we propose multilayer heterostructures on a SiO2 substrate, which provides multiple reflective bands with the very high reflective efficiency of nearly up to 100%. Electric field distributes mostly in the superficial layer. The proposed structure provides highly multiband reflection covering the emission peaks of QDs in LEDs; hence, it can eventually enhance QDs' fluorescence and enhance the brightness of QD-LEDs. We investigate four typical emission wavelengths, mainly aiming for red QD-LEDs and infrared QD-LEDs, which correspond to the applications of displays, infrared illumination, optical communication, and so on. The total reflection bands can be adjusted according to practical requirements by tuning the thickness of every layer. One fabrication procedure can be used for different kinds of QDs or the same kind of QD with different sizes without changing their processing properties. The proposed structure has fewer flat layers compared with 1-D photonic crystals, which leads to lower cost and easier fabrications.

Max-margin deepwalk: discriminative learning of network representation

Abstract: DeepWalk is a typical representation learning method that learns low-dimensional representations for vertices in social networks. Similar to other network representation learning (NRL) models, it encodes the network structure into vertex representations and is learnt in unsupervised form. However, the learnt representations usually lack the ability of discrimination when applied to machine learning tasks, such as vertex classification. In this paper, we overcome this challenge by proposing a novel semi-supervised model, max-margin Deep-Walk (MMDW). MMDW is a unified NRL framework that jointly optimizes the max-margin classifier and the aimed social representation learning model. Influenced by the max-margin classifier, the learnt representations not only contain the network structure, but also have the characteristic of discrimination. The visualizations of learnt representations indicate that our model is more discriminative than unsupervised ones, and the experimental results on vertex classification demonstrate that our method achieves a significant improvement than other state-of-the-art methods. The source code can be obtained from https://github.com/thunlp/MMDW.

Big data-driven optimization for mobile networks toward 5G

Abstract: Big data offers a plethora of opportunities to mobile network operators for improving quality of service. This article explores various means of integrating big data analytics with network optimization toward the objective of improving the user quality of experience. We first propose a framework of Big Data-Driven (BDD) mobile network optimization. We then present the characteristics of big data that are collected not only from user equipment but also from mobile networks. Moreover, several techniques in data collection and analytics are discussed from the viewpoint of network optimization. Certain user cases on the application of the proposed framework for improving network performance are also given in order to demonstrate the feasibility of the framework. With the integration of the emerging fifth generation (5G) mobile networks with big data analytics, the quality of our daily mobile life is expected to be tremendously enhanced.

Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering

Abstract: State-of-the-art subspace clustering methods are based on expressing each data point as a linear combination of other data points while regularizing the matrix of coefficients with l1, l2 or nuclear norms. l1 regularization is guaranteed to give a subspace-preserving affinity (i.e., there are no connections between points from different subspaces) under broad theoretical conditions, but the clusters may not be connected. l2 and nuclear norm regularization often improve connectivity, but give a subspace-preserving affinity only for independent subspaces. Mixed l1, l2 and nuclear norm regularizations offer a balance between the subspacepreserving and connectedness properties, but this comes at the cost of increased computational complexity. This paper studies the geometry of the elastic net regularizer (a mixture of the l1 and l2 norms) and uses it to derive a provably correct and scalable active set method for finding the optimal coefficients. Our geometric analysis also provides a theoretical justification and a geometric interpretation for the balance between the connectedness (due to l2 regularization) and subspace-preserving (due to l1 regularization) properties for elastic net subspace clustering. Our experiments show that the proposed active set method not only achieves state-of-the-art clustering performance, but also efficiently handles large-scale datasets.

A Comprehensive Review of Smart Energy Meters in Intelligent Energy Networks

Abstract: The significant increase in energy consumption and the rapid development of renewable energy, such as solar power and wind power, have brought huge challenges to energy security and the environment, which, in the meantime, stimulate the development of energy networks toward a more intelligent direction. Smart meters are the most fundamental components in the intelligent energy networks (IENs). In addition to measuring energy flows, smart energy meters can exchange the information on energy consumption and the status of energy networks between utility companies and consumers. Furthermore, smart energy meters can also be used to monitor and control home appliances and other devices according to the individual consumer’s instruction. This paper systematically reviews the development and deployment of smart energy meters, including smart electricity meters, smart heat meters, and smart gas meters. By examining various functions and applications of smart energy meters, as well as associated benefits and costs, this paper provides insights and guidelines regarding the future development of smart meters.

Interfacial Properties of Monolayer and Bilayer MoS2 Contacts with Metals: Beyond the Energy Band Calculations

Abstract: Although many prototype devices based on two-dimensional (2D) MoS2 have been fabricated and wafer scale growth of 2D MoS2 has been realized, the fundamental nature of 2D MoS2-metal contacts has not been well understood yet. We provide a comprehensive ab initio study of the interfacial properties of a series of monolayer (ML) and bilayer (BL) MoS2-metal contacts (metal = Sc, Ti, Ag, Pt, Ni, and Au). A comparison between the calculated and observed Schottky barrier heights (SBHs) suggests that many-electron effects are strongly suppressed in channel 2D MoS2 due to a charge transfer. The extensively adopted energy band calculation scheme fails to reproduce the observed SBHs in 2D MoS2-Sc interface. By contrast, an ab initio quantum transport device simulation better reproduces the observed SBH in 2D MoS2-Sc interface and highlights the importance of a higher level theoretical approach beyond the energy band calculation in the interface study. BL MoS2-metal contacts generally have a reduced SBH than ML MoS2-metal contacts due to the interlayer coupling and thus have a higher electron injection efficiency.

Question Answering on Freebase via Relation Extraction and Textual Evidence

Abstract: Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic parsing, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a neural network based relation extractor to retrieve the candidate answers from Freebase, and then infer over Wikipedia to validate these answers. Experiments on the WebQuestions question answering dataset show that our method achieves an F_1 of 53.3%, a substantial improvement over the state-of-the-art.

Sketch-based image retrieval via Siamese convolutional neural network

Abstract: Sketch-based image retrieval (SBIR) is a challenging task due to the ambiguity inherent in sketches when compared with photos. In this paper, we propose a novel convolutional neural network based on Siamese network for SBIR. The main idea is to pull output feature vectors closer for input sketch-image pairs that are labeled as similar, and push them away if irrelevant. This is achieved by jointly tuning two convolutional neural networks which linked by one loss function. Experimental results on Flickr15K demonstrate that the proposed method offers a better performance when compared with several state-of-the-art approaches.

Monolayer Phosphorene–Metal Contacts

Abstract: Recently, phosphorene electronic and optoelectronic prototype devices have been fabricated with various metal electrodes. We systematically explore for the first time the contact properties of monolayer (ML) phosphorene with a series of commonly used metals in a transistor by using both ab initio electronic structure calculations and more reliable quantum transport simulations. ML phosphorene undergoes a metallization under the checked metals, and the metallized ML phosphorenes have an unnegligible coupling with channel ML phosphorene. ML phosphorene forms an n-type Schottky contact with Au, Cu, Cr, Al, and Ag electrodes and a p-type Schottky contact with Ti, Ni, and Pd electrodes upon inclusion of such a coupling. The calculated Schottky barrier heights are in good agreement with the available experimental data with Ni and Ti as electrodes. Our findings not only provide an insight into the ML phosphorene–metal interfaces but also help in ML phosphorene based device design.

Cloud radio access network: Virtualizing wireless access for dense heterogeneous systems

Abstract: Cloud radio access network (C-RAN) refers to the visualization of base station functionalities by means of cloud computing. This results in a novel cellular architecture in which low-cost wireless access points, known as radio units or remote radio heads, are centrally managed by a reconfigurable centralized "cloud", or central, unit. C-RAN allows operators to reduce the capital and operating expenses needed to deploy and maintain dense heterogeneous networks. This critical advantage, along with spectral efficiency, statistical multiplexing and load balancing gains, make C-RAN well positioned to be one of the key technologies in the development of 5G systems. In this paper, a succinct overview is presented regarding the state of the art on the research on C-RAN with emphasis on fronthaul compression, baseband processing, medium access control, resource allocation, system-level considerations and standardization efforts.

On the Outage Probability of Device-to-Device-Communication-Enabled Multichannel Cellular Networks: An RSS-Threshold-Based Perspective

Abstract: In this paper, we study the outage probability of device-to-device (D2D)-communication-enabled cellular networks from a general threshold-based perspective. Specifically, a mobile user equipment (UE) transmits in D2D mode if the received signal strength (RSS) from the nearest base station (BS) is less than a specified threshold $\beta \ge 0$ ; otherwise, it connects to the nearest BS and transmits in cellular mode. The RSS-threshold-based setting is general in the sense that by varying $\beta$ from $\beta = 0$ to $\beta = \infty$ , the network accordingly evolves from a traditional cellular network (including only cellular mode) toward a wireless ad hoc network (including only D2D mode). We provide a unified framework to analyze the downlink outage probability in a multichannel environment with Rayleigh fading, where the spatial distributions of BSs and UEs are well explicitly accounted for by utilizing stochastic geometry. We derive closed-form expressions for the outage probability of a generic UE and that in both cellular mode and D2D mode and quantify the performance gains in outage probability that can be obtained by allowing such RSS-threshold-based D2D communications. We show that increasing the number of channels, although able to support more cellular UEs, may result in an increase of outage probability in the D2D-enabled cellular network. The corresponding condition and reason are also identified by applying our framework.

Cluster Content Caching: An Energy-Efficient Approach to Improve Quality of Service in Cloud Radio Access Networks

Abstract: In cloud radio access networks (C-RANs), a substantial amount of data must be exchanged in both backhaul and fronthaul links, which causes high power consumption and poor quality of service (QoS) experience for real-time services. To solve this problem, a cluster content caching structure is proposed in this paper, which takes full advantages of distributed caching and centralized signal processing. In particular, redundant traffic on the backhaul can be reduced because the cluster content cache provides a part of required content objects for remote radio heads (RRHs) connected to a common edge cloud. Tractable expressions for both effective capacity and energy efficiency performance are derived, which show that the proposed structure can improve QoS guarantees with a lower cost of local storage. Furthermore, to fully explore the potential of the proposed cluster content caching structure, the joint design of resource allocation and RRH association is optimized, and two distributed algorithms are accordingly proposed. Simulation results verify the accuracy of the analytical results and show the performance gains achieved by cluster content caching in C-RANs.

Compact Dual-Polarized UWB Quasi-Self-Complementary MIMO/Diversity Antenna With Band-Rejection Capability

Abstract: A novel compact ultrawideband (UWB) multiple-input-multiple-output (MIMO) antenna system with dual polarization and band-rejection capabilities is proposed. The proposed MIMO antenna system consists of two quasi-self-complementary (QSC) antenna elements. The elements are arranged orthogonally and fed perpendicularly to obtain polarization diversity. High isolation can be achieved without additional decoupling structure owing to the inherent advantage of the self-complementary structure. Notched band at WLAN system can be realized by etching a bent slit in each of the radiating elements. Moreover, a four-element MIMO system is also proposed and investigated to fully reveal its potential use. Diversity performance in terms of envelope correlation coefficient (ECC) and the mean effective gain (MEG) ratio are studied. Measured results show that the proposed antenna has a wide bandwidth ranging from 3 to 12 GHz with band rejection at WLAN system and high port isolation (S 12 ≤ -20 dB at most of the band), which demonstrate the proposed MIMO/diversity antenna system can be a good candidate for UWB applications.

Finite-Time Consensus for Multiagent Systems With Cooperative and Antagonistic Interactions

Abstract: This paper deals with finite-time consensus problems for multiagent systems that are subject to hybrid cooperative and antagonistic interactions. Two consensus protocols are constructed by employing the nearest neighbor rule. It is shown that under the presented protocols, the states of all agents can be guaranteed to reach an agreement in a finite time regarding consensus values that are the same in modulus but may not be the same in sign. In particular, the second protocol can enable all agents to reach a finite-time consensus with a settling time that is not dependent upon the initial states of agents. Simulation results are given to demonstrate the effectiveness and finite-time convergence of the proposed consensus protocols.