Beijing University of Posts and Telecommunications

About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.

Fabrication of β-Ga2O3/ZnO heterojunction for solar-blind deep ultraviolet photodetection

Abstract: A thin-film type β-Ga2O3/ZnO heterojunction was constructed for the first time by radio frequency magnetron sputtering of a β-Ga2O3 layer on a (0001) ZnO single crystalline substrate. The heterojunction presents a typical positive rectification in the dark, and shows a solar-blind deep ultraviolet photoelectric characteristic. Under zero bias, the photodetector based on a β-Ga2O3/ZnO heterojunction exhibits an I photo/I dark ratio of ~14.8 under a 254 nm light illumination with a light intensity of 50 μW cm−2, showing a characteristic of working with zero power consumption. The photocurrent linear increases and the response time decreases with the increase of the light intensity. The photodetector shows a R λ of 0.35 A W−1 and an EQE of 1.7 × 102% under 254 nm illumination of 50 μW cm−2 and a negative bias of 5 V. This study presents a promising candidate for use in solar-blind deep ultraviolet photodetection.

A Survey of Collaborative Filtering-Based Recommender Systems for Mobile Internet Applications

Abstract: With the rapid development and application of the mobile Internet, huge amounts of user data are generated and collected every day How to take full advantages of these ubiquitous data is becoming the essential aspect of a recommender system Collaborative filtering (CF) has been widely studied and utilized to predict the interests of mobile users and to make proper recommendations In this paper, we first propose a framework of the CF recommender system based on various user data including user ratings and user behaviors Key features of these two kinds of data are discussed Moreover, several typical CF algorithms are classified as memory-based approaches and model-based approaches and compared Two case studies are presented in an effort to validate the proposed framework

Energy-Efficient Resource Allocation Optimization for Multimedia Heterogeneous Cloud Radio Access Networks

Abstract: The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm that incorporates cloud computing into heterogeneous networks (HetNets), thereby taking full advantage of cloud radio access networks (C-RANs) and HetNets. Characterizing cooperative beamforming with fronthaul capacity and queue stability constraints is critical for multimedia applications to improve the energy efficiency (EE) in H-CRANs. An energy-efficient optimization objective function with individual fronthaul capacity and intertier interference constraints is presented in this paper for queue-aware multimedia H-CRANs. To solve this nonconvex objective function, a stochastic optimization problem is reformulated by introducing the general Lyapunov optimization framework. Under the Lyapunov framework, this optimization problem is equivalent to an optimal network-wide cooperative beamformer design algorithm with instantaneous power, average power, and intertier interference constraints, which can be regarded as a weighted sum EE maximization problem and solved by a generalized weighted minimum mean-square error approach. The mathematical analysis and simulation results demonstrate that a tradeoff between EE and queuing delay can be achieved, and this tradeoff strictly depends on the fronthaul constraint.

Enhanced Energy-Efficient Scheduling for Parallel Applications in Cloud

Abstract: Energy consumption has become a major concern to the widespread deployment of cloud data centers. The growing importance for parallel applications in the cloud introduces significant challenges in reducing the power consumption drawn by the hosted servers. In this paper, we propose an enhanced energy-efficient scheduling (EES) algorithm to reduce energy consumption while meeting the performance-based service level agreement (SLA). Since slacking non-critical jobs can achieve significant power saving, we exploit the slack room and allocate them in a global manner in our schedule. Using random generated and real-life application workflows, our results demonstrate that EES is able to reduce considerable energy consumption while still meeting SLA.

Fast Pairing of Device-to-Device Link Underlay for Spectrum Sharing With Cellular Users

Abstract: Device-to-device (D2D) communications has attracted substantial research attention recently owing to its simplicity and its potential to improve spectrum and energy efficiency within the existing cellular infrastructure. This work presents a low complexity method for matching D2D links with cellular user equipments (CUEs) to form partners for spectrum sharing. The overall objective is to maximize well defined performance metrics of all D2D links and CUEs after successful pairing under power and QoS constraints. We begin by identifying a set of D2D link candidates for each CUE uplink for a requisite SINR level to reduce the number of potential pairing searches. More importantly, we present a simple pairing algorithm to reduce the computational cost well below the well known Hungarian (Kuhn-Munkres) algorithm used in the literature for the pairing problem. Our new algorithm exhibits low complexity and is effective in improving the sum rate of all spectrum sharing CUEs and D2D links with modest performance loss.