Polymer Optical Fibers (POF) Applications for Indoor Cell Coverage Transmission Infrastructure

2009 ◽  
pp. 1178-1187
Author(s):  
Spiros Louvros ◽  
Athanassios C. Iossifides
Keyword(s):  
Network Topology ◽  
Graphical Representation ◽  
Network Evolution ◽  
Transmission Network ◽  
Network Growth ◽  
Technology Roadmap ◽  
Target Network ◽  
Cell Plan ◽  
Polymer Optical Fibers ◽  
Topology Selection

The role of transmission network design is diverse. Basically, it includes the preparation of transmission solutions for access and core (backbone) transmission networks. In the design of a transmission network, the engineer must have knowledge about existing transmission products and also operator budget analysis. For this reason, the transmission engineer might also act in early discussions with an operator and, in that case, support the marketing unit with technical competence within the area of transmission. In GSM/GPRS networks the user traffic is circuit switched through the GSM network and the signaling messages (including SMS) are transported on dedicated circuits, while the packet traffic is packet switched through the GPRS infrastructure. One of the most important parameters to consider is the design of GSM/PRS networks is the access radio topology. The network topology selection is an evaluation process, which incorporates business strategy, investment costs, technology roadmap, network redundancy and robustness, network evolution path, and the migration strategy from the current network to the planned target network. The topology selection produces a preferred network topology plan for the target network. The topology provides information about the network such as node/site location, geographical information, existing network infrastructure, and capacity, new node/site to be added, and new network configuration, such as new hub sites. The information contained in the topology plan allows the radio transmission network planner to formulate an expansion strategy to meet future cellular network growth (Figure 1). Cell plan is a graphical representation of the network which simply looks like a cell pattern on a map. However, there is a lot of work behind it, regarding the correct geographical position of the site, the antenna parameters and types, the dimensioning analysis regarding the offered and designed capacity and interference predictions. Such planning needs computer-aided analysis tools for radio propagation studies, for example, planning tools like TEMS CellPlanner Universal or NetHawk analyzer.

scholarly journals Specialization Models of Network Growth

2018 ◽  
Vol 7 (3) ◽  
pp. 375-392 ◽  
Author(s):  
L A Bunimovich ◽  
D C Smith ◽  
B Z Webb
Keyword(s):  
Power Law ◽  
Network Topology ◽  
Real World ◽  
Degree Distribution ◽  
Small World ◽  
Hierarchical Network ◽  
Network Growth ◽  
Complex Tasks ◽  
Small World Property ◽  
Law Degree

AbstractOne of the most important features observed in real networks is that, as a network’s topology evolves so does the network’s ability to perform various complex tasks. To explain this, it has also been observed that as a network grows certain subnetworks begin to specialize the function(s) they perform. Herein, we introduce a class of models of network growth based on this notion of specialization and show that as a network is specialized using this method its topology becomes increasingly sparse, modular and hierarchical, each of which are important properties observed in real networks. This procedure is also highly flexible in that a network can be specialized over any subset of its elements. This flexibility allows those studying specific networks the ability to search for mechanisms that describe their growth. For example, we find that by randomly selecting these elements a network’s topology acquires some of the most well-known properties of real networks including the small-world property, disassortativity and a right-skewed degree distribution. Beyond this, we show how this model can be used to generate networks with real-world like clustering coefficients and power-law degree distributions, respectively. As far as the authors know, this is the first such class of models that can create an increasingly modular and hierarchical network topology with these properties.

Network topology selection with multistate neural memories

2015 ◽  
Vol 42 (6) ◽  
pp. 3219-3226 ◽  
Author(s):  
Y. Sinan Hanay ◽  
Shin’ichi Arakawa ◽  
Masayuki Murata
Keyword(s):  
Network Topology ◽  
Topology Selection

Fault Diagnosis in Transmission Network Based on Optimization Technique

2012 ◽  
Vol 433-440 ◽  
pp. 3395-3399
Author(s):  
Hong Bo Cheng
Keyword(s):  
Genetic Algorithms ◽  
Fault Diagnosis ◽  
Power System ◽  
Network Topology ◽  
Optimization Technique ◽  
Transmission Network ◽  
Integer Programming Problem ◽  
Heuristic Knowledge ◽  
Scada System ◽  
Configuration Information

The fault diagnosis in power system is treated as a 0-1 integer programming problem use the switching and action information collected by SCADA system, combined with the analysis of protect. A comprehensive objective function has been established and genetic algorithms has been used to solve it. This approach has taken fully advantage of the characteristics of the power system's protection and the network topology configuration information. The optimized method is used to locate the fault as possible as fast. Rigorous theory of the method does not require the introduction of heuristic knowledge, and it can adapt to changes in network topology, and can used both in the single failure of power system and multiple failures in power system too.

scholarly journals Applying machine learning in network topology optimization

2021 ◽  
Vol 2 (4) ◽  
pp. 91-99
Author(s):  
Zhouwei Gang ◽  
Qianyin Rao ◽  
Lin Guo ◽  
Lin Xi ◽  
Zezhong Feng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Topology Optimization ◽  
Human Resources ◽  
Network Topology ◽  
Absorption Capacity ◽  
Transmission Network ◽  
Breadth First Search ◽  
Communication Services ◽  
Node Number ◽  
Network Topology Optimization

Nowadays, telecommunications have become an indispensable part of our life, 5G technology brings better network speeds, helps the AR and VR industry, and connects everything. It will deeply change our society. Transmission is the vessel of telecommunications. While the vessel is not so healthy, some of them are overloaded, meanwhile, others still have lots of capacity. It not only affects the customer experience, but also affects the development of communication services because of a resources problem. A transmission network is composed of transmission nodes and links. So that the possible topology numbers equal to node number multiplied by number of links means it is impossible for humans to optimize. We use Al instead of humans for topology optimization. The AI optimization solution uses an ITU Machine Learning (ML) standard, Breadth-First Search (BFS) greedy algorithm and other mainstream algorithms to solve the problem. It saves a lot of money and human resources, and also hugely improves traffic absorption capacity. The author comes from the team named "No Boundaries". The team attend ITU AI/ML in 5G Challenge and won the Gold champions (1st place).

scholarly journals Power-Based Concept for Current Injection by Inverter-Interfaced Distributed Generations during Transmission-Network Faults

2021 ◽  
Vol 11 (21) ◽  
pp. 10437
Author(s):  
Boštjan Polajžer ◽  
Bojan Grčar ◽  
Jernej Černelič ◽  
Jožef Ritonja
Keyword(s):  
Network Topology ◽  
Reactive Power ◽  
Short Circuit ◽  
Current Injection ◽  
Fault Current ◽  
Transmission Network ◽  
Transmission Networks ◽  
Distributed Generations ◽  
Grid Codes ◽  
Radial Network

This paper analyzes the influence of inverter-interfaced distributed generations’ (IIDGs) response during transmission network faults. The simplest and safest solution is to switch IIDGs off during network faults without impacting the network voltages. A more elaborate and efficient concept, required by national grid codes, is based on controlling the IIDGs’ currents, involving positive- and negative-sequence voltage measured at the connection point. In this way the magnitude and phase of the injected currents can be adjusted, although the generated power will depend on the actual line voltages at the connection point. Therefore, an improved concept is proposed to adjust IIDGs’ fault current injection through the required active and reactive power, employing the same voltage characteristics. The proposed, i.e., power-based concept, is more definite than the current-based one, since the required power will always be generated. The discussed concepts for the fault current injection by IIDGs were tested in different 110-kV networks with loop and radial topologies, and for different short-circuit capabilities of the aggregated network supply. Based on extensive numerical calculations, the power-based concept during transmission networks faults generates more reactive power compared to the current-based concept. However, the voltage support by IIDGs during transmission networks faults, regardless of the concept being used, is influenced mainly by the short-circuit capability of the aggregated network supply. As regards distance protection operation, it is influenced additionally by the network topology, i.e., in radial network topology, the remote relay’s operation can be delayed due to a largely seen impedance.

scholarly journals The evolution of network topology by selective removal

2005 ◽  
Vol 2 (5) ◽  
pp. 533-536 ◽  
Author(s):  
Marcel Salathé ◽  
Robert M May ◽  
Sebastian Bonhoeffer
Keyword(s):  
Network Topology ◽  
Biological Networks ◽  
Preferential Attachment ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Network Growth ◽  
Scale Free ◽  
The Past ◽  
The World ◽  
Growing Networks

The topology of large social, technical and biological networks such as the World Wide Web or protein interaction networks has caught considerable attention in the past few years (reviewed in Newman 2003 ), and analysis of the structure of such networks revealed that many of them can be classified as broad-tailed, scale-free-like networks, since their vertex connectivities follow approximately a power-law. Preferential attachment of new vertices to highly connected vertices is commonly seen as the main mechanism that can generate scale-free connectivity in growing networks ( Watts 2004 ). Here, we propose a new model that can generate broad-tailed networks even in the absence of network growth, by not only adding vertices, but also selectively eliminating vertices with a probability that is inversely related to the sum of their first- and second order connectivity.

scholarly journals The Intertemporal Evolution Model of Enterprise R&D Cooperative Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Rongguo Qiu ◽  
Yutong Wang ◽  
Tingqiang Chen
Keyword(s):  
Steady State ◽  
Technological Innovation ◽  
Network Topology ◽  
Previous History ◽  
Stable State ◽  
Network Evolution ◽  
Evolution Model ◽  
Practical Significance ◽  
Cooperative Research ◽  
Cooperation Network

Cooperation plays an irreplaceable role in knowledge creation and innovation. Innovation cooperation among enterprises forms a complex network of enterprise R&D. Given the intertemporal R&D network evolution and the complex influence between stock knowledge, this study constructs a discrete indefinitely intertemporal evolution model of an enterprise R&D cooperation network. The model consists of two main parts, that is, first is how technological innovation depends on the structure of enterprise R&D cooperation network and the second is how the enterprise R&D cooperation network evolves according to the level of technological innovation. This work uses calculation experiment and simulation method to study the evolution characteristics of enterprise R&D network in different initial R&D network topology structures, such as Erdos–Renyi random graph, WS small-world network, and BA scale-free network, and determine how previous history, attractiveness, and reputation for enterprise influence the steady-state characteristics of R&D network evolution. Results show that (1) when the R&D network evolution reaches a stable state, the joint distribution of stock knowledge and the number of cooperative enterprises do not affect the initial R&D network topology. However, the evolution path of the enterprise R&D network is complicated by the initial R&D network topology. (2) Among the three factors through which enterprises make decisions, if the enterprise values previous history highly, then the stock knowledge in a steady state will dissipate; if the enterprise values reputation highly, then the stock knowledge in a steady state will decrease but always above a threshold; if the enterprise values attractiveness highly, then the stock knowledge in a steady state can rise to a high level. These conclusions have important theoretical values and practical significance for the promotion of enterprise scientific and technological innovation and cooperative research.

Sign in / Sign up

Export Citation Format

Share Document