Comparison of Converged Packet-Optical Core Network Architectures

2014 ◽  
Author(s):  
Serge Melle ◽  
Satyajeet Ahuja ◽  
Onur Turkcu ◽  
Steven J. Hand
Keyword(s):  
Network Architectures ◽  
Core Network

scholarly journals SDN/NFV-Based Mobile Packet Core Network Architectures: A Survey

2017 ◽  
Vol 19 (3) ◽  
pp. 1567-1602 ◽  
Author(s):  
Van-Giang Nguyen ◽  
Anna Brunstrom ◽  
Karl-Johan Grinnemo ◽  
Javid Taheri
Keyword(s):  
Network Architectures ◽  
Core Network ◽  
Mobile Packet Core

A Power Consumption Analysis for IP-Over-WDM Core Network Architectures

2012 ◽  
Vol 4 (2) ◽  
pp. 108 ◽  
Author(s):  
Francesco Musumeci ◽  
Massimo Tornatore ◽  
Achille Pattavina
Keyword(s):  
Power Consumption ◽  
Network Architectures ◽  
Core Network ◽  
Ip Over Wdm

scholarly journals Optimising Differentiated Services Strategies under IMS

2018 ◽  
Vol 2 (3) ◽  
pp. 1-10
Author(s):  
David K. Osei-Aboagye ◽  
Peter S. Excell
Keyword(s):  
Mobile Communications ◽  
Network Architecture ◽  
Discrete Event ◽  
Differentiated Services ◽  
Main Concern ◽  
Network Architectures ◽  
Network Simulator ◽  
Core Network ◽  
Network Technologies

The evolving standards of mobile communications, the wide variety of services they offer and the rapid growth of the Internet have made a merger of the two network technologies inevitable. One of the most prominent platforms that has been developed to facilitate this is the IP Multimedia Subsystem (IMS) concept. Many mobile communications standards integrate IMS as the main core network architecture and Quality of Service (QoS) is the main concern for customer satisfaction. A major approach to optimisation of QoS is the Differentiated Services scheme, and a simulation study of implementations of this is presented. The study covered an IMS core network architecture modelled with discrete-event network simulator software, with a Differentiated Services QoS scheme run over it with differing bearer traffic scenarios. Implications for core network architectures are discussed.

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

Enabling Things To Talk in Heterogeneous Self-Organizing Networks—An Exhaustive Critique For IOT

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 728-750
Author(s):  
Naeem Z Azeemi ◽  
Saira Khan ◽  
Sharmini Enoch ◽  
Riktesh Srivastava ◽  
Omar al Basheer ◽  
...  
Keyword(s):  
Mobile Networks ◽  
Adaptive Modulation ◽  
Sustainable Growth ◽  
Adaptive Modulation And Coding ◽  
Network Architectures ◽  
Multiple Antenna ◽  
Multiple Access Schemes ◽  
Natural Outcome ◽  
Air Interfaces ◽  
The Internet Of Things

The superstructure network in the Internet of Things (IoT) is an emerging network targeted to enable an ecosystem of smart applications and services. It connectsphysical resources and peopletogether with software, hence contribute to sustainable growth, provided it combines and guarantees trustand security for people and businesses.  In this work we presented smart city viewpoint opt-in to the Firth Generation (5G) mobile networks. Both a framework and deployment are explored rigorously to assist and predicting robustness of IoT technologies and applications as a natural outcome of the Third Generation Partnership Project (3GPP) in general and LTE in particular. These technologies are compared on the basis of Air Interfaces and their Specifications i.e. Adaptive Modulation and Coding, Multiple Access Schemes and Multiple Antenna Techniques along with the evolution and comparison of the Network Architectures.

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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