It is extensively approved that Channel State Information(CSI) plays an important role for synergetic transmission and interference management. However, pilot overhead to obtain CSI with enough precision is a signific...It is extensively approved that Channel State Information(CSI) plays an important role for synergetic transmission and interference management. However, pilot overhead to obtain CSI with enough precision is a significant issue for wireless communication networks with massive antennas and ultra-dense cell. This paper proposes a learning- based channel model, which can estimate, refine, and manage CSI for a synergetic transmission system. It decomposes the channel impulse response into multiple paths, and uses a learning-based algorithm to estimate paths' parameters without notable degradation caused by sparse pilots. Both indoor measurement and outdoor measurement are conducted to verify the feasibility of the proposed channel model preliminarily.展开更多
E-mail communication network evolution model based on user information propagation is studied. First, mathematical representation of weighted e-mail communication network is proposed, and network center parameters of ...E-mail communication network evolution model based on user information propagation is studied. First, mathematical representation of weighted e-mail communication network is proposed, and network center parameters of Enron dataset and the distribution of node degree and strength are analyzed. Then, some rules of e-mail communication network evolution are found. Second, the model of e-mail information propagation is described, and e-mail communication network evolution model based on user information propagation is proposed. Lastly, the simulation proves the correctness of the distribution characteristic of degree and strength of the model proposed and then verifies that the model proposed is closer to the real situation of e-mail communication network through parameter comparison. This research provides the basis for other researches on social network evolution and data communication.展开更多
The human brain is built to process complex visual impressions within milliseconds. In comparison with sequentially coded spoken language and written texts, we are capable of consuming graphical information at a high ...The human brain is built to process complex visual impressions within milliseconds. In comparison with sequentially coded spoken language and written texts, we are capable of consuming graphical information at a high bandwidth in a parallel fashion, producing a picture worth more than a thousand words. Effective information visualization can be a powerful tool to capture people's attention and quickly communicate large amounts of data and complex information. This is particularly important in the context of communication data, which often describes entities (people, organizations) and their connections through communication. Visual analytics approaches can optimize the user-computer interaction to gain insights into communication networks and learn about their structures. Network visualization is a perfect instrument to better communicate the results of analysis. The precondition for effective information visualization and successful visual reasoning is the capability to draw "good" pictures. Even though communication networks are often large, including thousands or even millions of people, underlying visualization principles are identical to those used for visualizing smaller networks. In this article, you will learn about these principles, giving you the ability to assess the quality of network visualizations and to draw better network pictures by yourself.展开更多
基金supported by National Basic Research Program of China (NO 2012CB316002)China’s 863 Project (NO 2014AA01A703)+2 种基金National Major Projec (NO. 2014ZX03003002-002)Program for New Century Excellent Talents in University (NCET-13-0321)Tsinghua University Initiative Scientific Research Program (2011THZ02-2)
文摘It is extensively approved that Channel State Information(CSI) plays an important role for synergetic transmission and interference management. However, pilot overhead to obtain CSI with enough precision is a significant issue for wireless communication networks with massive antennas and ultra-dense cell. This paper proposes a learning- based channel model, which can estimate, refine, and manage CSI for a synergetic transmission system. It decomposes the channel impulse response into multiple paths, and uses a learning-based algorithm to estimate paths' parameters without notable degradation caused by sparse pilots. Both indoor measurement and outdoor measurement are conducted to verify the feasibility of the proposed channel model preliminarily.
基金sponsored by the National Natural Science Foundation of China under grant number No. 61100008, 61201084the China Postdoctoral Science Foundation under Grant No. 2013M541346+3 种基金Heilongiiang Postdoctoral Special Fund (Postdoctoral Youth Talent Program) under Grant No. LBH-TZ0504Heilongjiang Postdoctoral Fund under Grant No. LBH-Z13058the Natural Science Foundation of Heilongjiang Province of China under Grant No. QC2015076Funds for the Central Universities of China under grant number HEUCF100602
文摘E-mail communication network evolution model based on user information propagation is studied. First, mathematical representation of weighted e-mail communication network is proposed, and network center parameters of Enron dataset and the distribution of node degree and strength are analyzed. Then, some rules of e-mail communication network evolution are found. Second, the model of e-mail information propagation is described, and e-mail communication network evolution model based on user information propagation is proposed. Lastly, the simulation proves the correctness of the distribution characteristic of degree and strength of the model proposed and then verifies that the model proposed is closer to the real situation of e-mail communication network through parameter comparison. This research provides the basis for other researches on social network evolution and data communication.
文摘The human brain is built to process complex visual impressions within milliseconds. In comparison with sequentially coded spoken language and written texts, we are capable of consuming graphical information at a high bandwidth in a parallel fashion, producing a picture worth more than a thousand words. Effective information visualization can be a powerful tool to capture people's attention and quickly communicate large amounts of data and complex information. This is particularly important in the context of communication data, which often describes entities (people, organizations) and their connections through communication. Visual analytics approaches can optimize the user-computer interaction to gain insights into communication networks and learn about their structures. Network visualization is a perfect instrument to better communicate the results of analysis. The precondition for effective information visualization and successful visual reasoning is the capability to draw "good" pictures. Even though communication networks are often large, including thousands or even millions of people, underlying visualization principles are identical to those used for visualizing smaller networks. In this article, you will learn about these principles, giving you the ability to assess the quality of network visualizations and to draw better network pictures by yourself.
