• ETRI Journal
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• v.37 no.3
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• pp.450-459
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• 2015

Small cell networks, as an important evolution path for next-generation cellular networks, have drawn much attention. Different from the traditional base stations (BSs) always-on model, we proposed a BSs on-off model, where a new, simple expression for the probabilities of active BSs in a heterogeneous network is derived. This model is more suitable for application in practical networks. Based on this, we develop an analytical framework for the performance evaluation of small cell networks, adopting stochastic geometry theory. We derive the system coverage probability; average energy efficiency (AEE) and average uplink power consumption (AUPC) for different association strategies; maximum biased received power (MaBRP); and minimum association distance (MiAD). It is analytically shown that MaBRP is beneficial for coverage but will have some loss in energy saving. On the contrary, MiAD is not advocated from the point of coverage but is more energy efficient. The simulation results show that the use of range expansion in MaBRP helps to save energy but that this is not so in MiAD. Furthermore, we can achieve an optimal AEE by establishing an appropriate density of small cells.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.447-449
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• 2015

Although the link between activity in the nuclei of galaxy and galactic mergers has been under scrutiny for several years, it is still unclear to what extent and for which populations of active galaxies merger-triggered activity is relevant. The environments of AGN allow an indirect probe of the past merger history and future merger probability of these systems, suffering less from sensitivity issues when extended to higher redshifts than traditional morphological studies of AGN host galaxies. Here we present results from our investigation of the environment of radio selected sources out to a redshift z=2. We employ the first data release J-band catalog of the new near-IR Infrared Medium-Deep Survey (IMS), 1.4 GHz radio data from the Faint Images of the Radio Sky at Twenty-cm (FIRST) survey and a deep dedicated VLA survey of the VIMOS field, covering a combined total of 20 sq. degrees. At a flux limit of the combined radio catalog of 0.1 mJy, we probe over 8 orders of magnitude of radio luminosity. Using the second closest neighbor density parameters, we test whether active galaxies inhabit denser environments. We find evidence for a sub-population of radio-selected AGN that reside in significantly overdense environments at small scales, although we do not find significant overdensities for the bulk of our sample. We show that radio-AGN in the most underdense environments have vigorous ongoing star formation. We interpret these results in terms of the triggering and fuelling mechanism of radio-AGN.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.227-240
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• 2011

New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.