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Institution

Beijing University of Posts and Telecommunications

EducationBeijing, Beijing, China
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.


Papers
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Journal ArticleDOI
TL;DR: In this article, a large class of generalized variable-coefficient Korteweg-de Vries (KdV) models with external-force and perturbed/dissipative terms is studied.

164 citations

Journal ArticleDOI
TL;DR: Simulation results show how QUVoD is a highly efficient user-centric mobile VoD solution in urban vehicular networks in comparison with existing state-of-the-art solutions.
Abstract: Recently, many cities around the world have witnessed large-scale deployment of terrestrial broadcasting mobile television (TV) to vehicles. This service is similar to the cable or satellite TV already in the home, and user-centric interactive mobile Video-on-Demand (VoD) over urban vehicular networks is in fact expected. However, providing this new service with focus on user Quality of Experience (QoE) constitutes a significant challenge. This paper introduces a QoE-driven User-centric solution for VoD services in urban vehicular network environments (QUVoD). QUVoD relies on a multihomed hierarchical peer-to-peer (P2P) and vehicular ad-hoc network (VANET) architecture. Vehicles construct a low-layer VANET via Wireless Access in the Vehicular Environment interfaces; they also form an upper layer P2P Chord overlay on top of a cellular network via Fourth-Generation (4G) interfaces. A novel grouping-based storage strategy that uniformly distributes the video segments along the Chord overlay is proposed, reducing segment seeking traffic while also enabling load balancing. A novel segment seeking and multipath delivery scheme that achieves high lookup success rate and very good video data delivery efficiency is also introduced, which achieves high lookup success rate and very good video data delivery efficiency. Furthermore, a new speculation-based prefetching strategy is proposed, which analyses users' interactive viewing behavior and, by estimating video segment playback order, employs prefetching of the expected segments, smoothening the video playback. Simulation results show how QUVoD is a highly efficient user-centric mobile VoD solution in urban vehicular networks in comparison with existing state-of-the-art solutions.

164 citations

Journal ArticleDOI
01 Feb 2012
TL;DR: It is demonstrated that a combination of two negatively correlated objectives under the multi-objective framework usually leads to remarkably better performance compared with either of the orignal single objectives, including even many popular algorithms.
Abstract: Community detection in social network analysis is usually considered as a single objective optimization problem, in which different heuristics or approximate algorithms are employed to optimize a objective function that capture the notion of community. Due to the inadequacy of those single-objective solutions, this paper first formulates a multi-objective framework for community detection and proposes a multi-objective evolutionary algorithm for finding efficient solutions under the framework. After analyzing and comparing a variety of objective functions that have been used or can potentially be used for community detection, this paper exploits the concept of correlation between objective which charcterizes the relationship between any two objective functions. Through extensive experiments on both artifical and real networks, this paper demonstrates that a combination of two negatively correlated objectives under the multi-objective framework usually leads to remarkably better performance compared with either of the orignal single objectives, including even many popular algorithms..

164 citations

Proceedings ArticleDOI
08 Dec 2008
TL;DR: A Spectrum-Tree base On-Demand routing protocol (STOD-RP) is proposed where a spectrum-tree is built in each spectrum band to address the cooperation between spectrum decision and route selection in an efficient way.
Abstract: A unique challenge for routing in cognitive radio networks is the collaboration between the route selection and spectrum decision. To solve this problem, in this paper a Spectrum-Tree base On-Demand routing protocol (STOD-RP) is proposed where a spectrum-tree is built in each spectrum band. The formation of the spectrum-tree addresses the cooperation between spectrum decision and route selection in an efficient way. In addition, a new route metric is proposed as well as a fast and efficient spectrum-adaptive route recovery method. Simulation results show that our proposed STOD-RP reduces the control overhead and shortens the average end-to-end delay significantly.

164 citations

Proceedings ArticleDOI
16 May 2011
TL;DR: This paper survey's recent Mobile Cloud Computing architecture, application partition & offloading, and context-aware service, and a concept model has been proposed to analyze related research work.
Abstract: The rapid development of mobile computing and cloud computing trigger novel computing paradigm-----Mobile Cloud Computing. This paper review current research effort towards Mobile Computing. First, we present several challenges for the design of Mobile Cloud Computing service. Second, a concept model has been proposed to analyze related research work. Third, we survey recent Mobile Cloud Computing architecture, application partition & offloading, and context-aware service.

164 citations


Authors

Showing all 39925 results

NameH-indexPapersCitations
Jie Zhang1784857221720
Jian Li133286387131
Ming Li103166962672
Kang G. Shin9888538572
Lei Liu98204151163
Muhammad Shoaib97133347617
Stan Z. Li9753241793
Qi Tian96103041010
Xiaodong Xu94112250817
Qi-Kun Xue8458930908
Long Wang8483530926
Jing Zhou8453337101
Hao Yu8198127765
Mohsen Guizani79111031282
Muhammad Iqbal7796123821
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202394
2022533
20213,009
20203,720
20193,817
20183,297