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SUMMARYTriggered by the explosion of mobile traffic, 5G (5th Generation) cellular network requires evolution to increase the system rate 1000 times higher than the current systems in 10 years. Motivated by this common problem, there are several studies to integrate mm-wave access into current cellular networks as multi-band heterogeneous networks to exploit the ultra-wideband aspect of the mm-wave band. The authors of this paper have proposed comprehensive architecture of cellular networks with mmwave access, where mm-wave small cell basestations and a conventional macro basestation are connected to Centralized-RAN (C-RAN) to effectively operate the system by enabling power efficient seamless handover as well as centralized resource control including dynamic cell structuring to match the limited coverage of mm-wave access with high traffic user locations via user-plane/control-plane splitting. In this paper, to prove the effectiveness of the proposed 5G cellular networks with mm-wave access, system level simulation is conducted by introducing an expected future traffic model, a measurement based mm-wave propagation model, and a centralized cell association algorithm by exploiting the C-RAN architecture. The numerical results show the effectiveness of the proposed network to realize 1000 times higher system rate than the current network in 10 years which is not achieved by the small cells using commonly considered 3.5 GHz band. Furthermore, the paper also gives latest status of mm-wave devices and regulations to show the feasibility of using mm-wave in the 5G systems.

Permeability is one of the most important characteristics of hydrocarbon bearing formations. An accurate knowledge of permeability gives petroleum engineers a tool for efficiently managing the production process of a field. It is also one of the most important pieces of information in the design and management of enhanced recovery operations. Formation permeability is often measured in the laboratory from cores or evaluated from well test data. Core analysis and well test data, however, are only available from a few wells in afield. On the other hand, almost all wells are logged. In this study an artificial neural network has been designed that is able to predict the permeability of the formations using the data provided by geophysical well logs with good accuracy. Artificial neural network, a biologically inspired computing method, with its ability to learn, self-adjust, and be trained provide a powerful tool to solve problems that involve pattern recognition. Using well logs to predict permeability has been attempted in the past. The problems with previous approaches were mainly two fold, namely, the number of variables used (only one variable-porosity), and using regression analysis as the main tool for correlations. The approach introduced in this paper is an attempt to overcome these short comings. This is done, first, by using many variables from well logs that may provide information about the permeability. Second, by recognizing the existence of possible patterns between these variables and formation permeability using artificial neural networks. Neuralnets are analog, inherently parallel and distributive systems. These characteristics, which will be discussed in the paper, are the main characteristics that enable artificial neural networks to be successful in predicting the permeability in rocks using well log information. Introduction Acquiring knowledge on formation permeability has remained one of the fundamental challenges to petroleum engineers.

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