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Generation of Multiple Weights in the Opportunistic Beamforming Systems
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作者 Guangyue LU Lei ZHANG +1 位作者 Houquan YU chao shao 《Wireless Sensor Network》 2009年第3期189-195,共7页
A new scheme to generate multiple weights used in opportunistic beamforming (OBF) system is proposed to deal with the performance degradation due to the fewer active users in the OBF system. In the proposed scheme, on... A new scheme to generate multiple weights used in opportunistic beamforming (OBF) system is proposed to deal with the performance degradation due to the fewer active users in the OBF system. In the proposed scheme, only two mini-slots are employed to create effective channels, while more channel candidates can be obtained via linearly combining the two effective channels obtained during the two mini-slots, thus increas-ing the multiuser diversity and the system throughputs. The simulation results verify the effectiveness of the proposed scheme. 展开更多
关键词 OPPORTUNISTIC BEAMFORMING (OBF) MULTIUSER DIVERSITY System Throughputs SCHEDULING
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RiMOM-IM: A Novel Iterative Framework for Instance Matching 被引量:4
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作者 chao shao Lin-Mei Hu +3 位作者 Juan-Zi Li Zhi-Chun Wang Tonglee Chung Jun-Bo Xia 《Journal of Computer Science & Technology》 SCIE EI CSCD 2016年第1期185-197,共13页
Instance matching, which aims at discovering the correspondences of instances between knowledge bases, is a fundamental issue for the ontological data sharing and integration in Semantic Web. Although considerable ins... Instance matching, which aims at discovering the correspondences of instances between knowledge bases, is a fundamental issue for the ontological data sharing and integration in Semantic Web. Although considerable instance matching approaches have already been proposed, how to ensure both high accuracy and efficiency is still a big challenge when dealing with large-scale knowledge bases. This paper proposes an iterative framework, RiMOM-IM (RiMOM-Instance Matching). The key idea behind this framework is to fully utilize the distinctive and available matching information to improve the efficiency and control the error propagation. We participated in the 2013 and 2014 competition of Ontology Alignment Evaluation Initiative (OAEI), and our system was ranked the first. Furthermore, the experiments on previous OAEI datasets also show that our system performs the best. 展开更多
关键词 instance matching large-scale knowledge base BLOCKING similarity aggregation
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