On the optimal routing for Reconfigurable Network Architecture

2014 ◽  
Author(s):  
Kai Pan ◽  
Hui Li ◽  
Weiyang Liu ◽  
Zhipu Zhu ◽  
Bing Zhu
Keyword(s):  
Network Architecture ◽  
Optimal Routing

scholarly journals An Optimal Routing Algorithm in Service Customized 5G Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Haipeng Yao ◽  
Chao Fang ◽  
Yiru Guo ◽  
Chenglin Zhao
Keyword(s):  
Big Data ◽  
Network Architecture ◽  
Network Performance ◽  
Routing Algorithm ◽  
Optimal Routing ◽  
Cache Replacement ◽  
Data Traffic ◽  
Average Response ◽  
5G Networks ◽  
Content Centric Networking

With the widespread use of Internet, the scale of mobile data traffic grows explosively, which makes 5G networks in cellular networks become a growing concern. Recently, the ideas related to future network, for example, Software Defined Networking (SDN), Content-Centric Networking (CCN), and Big Data, have drawn more and more attention. In this paper, we propose a service-customized 5G network architecture by introducing the ideas of separation between control plane and data plane, in-network caching, and Big Data processing and analysis to resolve the problems traditional cellular radio networks face. Moreover, we design an optimal routing algorithm for this architecture, which can minimize average response hops in the network. Simulation results reveal that, by introducing the cache, the network performance can be obviously improved in different network conditions compared to the scenario without a cache. In addition, we explore the change of cache hit rate and average response hops under different cache replacement policies, cache sizes, content popularity, and network topologies, respectively.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Distributed Aggregation/Disaggregation Algorithms for Optimal Routing in Data Networks

1988 ◽  
Author(s):  
Wei K. Tsai ◽  
G. Huang ◽  
John K. Antonio ◽  
Wei-T. Tsai
Keyword(s):  
Data Networks ◽  
Optimal Routing

A Review on 3GPP 5G Security Aspects

2019 ◽  
Author(s):  
Rajavelsamy R ◽  
Debabrata Das
Keyword(s):  
Network Architecture ◽  
Base Station ◽  
Cellular Systems ◽  
Core Network ◽  
User Privacy ◽  
Wireless Backhaul ◽  
Level Of Use ◽  
Unified Authentication ◽  
3Rd Generation Partnership Project ◽  
5G Security

5G promises to support new level of use cases that will deliver a better user experience. The 3rd Generation Partnership Project (3GPP) [1] defined 5G system introduced fundamental changes on top of its former cellular systems in several design areas, including security. Unlike in the legacy systems, the 5G architecture design considers Home control enhancements for roaming customer, tight collaboration with the 3rd Party Application servers, Unified Authentication framework to accommodate various category of devices and services, enhanced user privacy, and secured the new service based core network architecture. Further, 3GPP is investigating the enhancements to the 5G security aspects to support longer security key lengths, False Base station detection and wireless backhaul in the Phase-2 of 5G standardization [2]. This paper provides the key enhancements specified by the 3GPP for 5G system, particularly the differences to the 4G system and the rationale behind the decisions.

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