scholarly journals Automatic Distributing Schemes of Physical Cell Identity for Self-Organizing Networks

2012 ◽  
Vol 8 (10) ◽  
pp. 973713 ◽  
Author(s):  
Yao Wei ◽  
Mugen Peng ◽  
Wenbo Wang ◽  
Shijun Min ◽  
Jia Mo Jiang ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Layered Structure ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
The Self ◽  
Correlation Property ◽  
Cell Identity ◽  
Simulation Results ◽  
Physical Cell Identity ◽  
Self Organizing

This paper presents and puts forward an optimal automatic distributing of physical cell identity (ADPCI) scheme for the self-organizing network (SON). Considering the high number and the layered structure of the evolved node B (eNodeB, eNB) in the initial rollout phase, the assigning of PCI for cells would be quite complex. The PCI self-distributing problem is mapped to the well-known minimum spanning tree (MST) problem in order to optimize the PCI reuse distance and decrease the multiplexing interference. The correlation property of PCI is analyzed and taken into consideration in the assigning phase. Moreover, a suboptimal algorithm (SADPCI) is presented as it performs approximately to ADPCI but the computational complexity is lower. To demonstrate the proposal validity, performances of ADPCI and SADPCI are evaluated. Simulation results illustrate that these schemes can achieve significantly higher performance even under the condition of severe PCI deficiency.

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

Modeling and Production of Robot Trajectories Using the Temporal Parametrized Self Organizing Maps

2003 ◽  
Vol 13 (02) ◽  
pp. 119-127 ◽  
Author(s):  
Antonio Carlos Padoan ◽  
Guilherme de A. Barreto ◽  
Aluizio F. R. Araújo
Keyword(s):  
Inverse Kinematics ◽  
The Self ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Neural Architecture ◽  
New States ◽  
Simulation Results ◽  
Self Organizing

In this paper we proposed an unsupervised neural architecture, called Temporal Parametrized Self Organizing Map (TEPSOM), capable of learning and reproducing complex robot trajectories and interpolating new states between the learned ones. The TEPSOM combines the Self-Organizing NARX (SONARX) network, responsible for coding the temporal associations of the robotic trajectory, with the Parametrized Self-Organizing (PSOM) network, responsible for an efficient interpolation mechanism acting on the SONARX neurons. The TEPSOM network is used to model the inverse kinematics of the PUMA 560 robot during the execution of trajectories with repeated states. Simulation results show that the TEPSOM is more accurate than the SONARX in the reproduction of the learned trajectories.

Visualization of Complex Datasets with the Self-Organizing Spanning Tree

2015 ◽  
pp. 209-217 ◽  
Author(s):  
Ezequiel López-Rubio ◽  
Esteban José Palomo ◽  
Rafael Marcos Luque Baena ◽  
Enrique Domínguez
Keyword(s):  
Spanning Tree ◽  
The Self ◽  
Self Organizing ◽  
Complex Datasets

Path Optimality Conditions for Minimum Spanning Tree Problem with Uncertain Edge Weights

2015 ◽  
Vol 23 (01) ◽  
pp. 49-71 ◽  
Author(s):  
Jian Zhou ◽  
Lu Chen ◽  
Ke Wang
Keyword(s):  
Computational Complexity ◽  
Optimality Conditions ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Uncertain Graph ◽  
Numerical Examples ◽  
Low Computational Complexity ◽  
Uncertain Variables ◽  
Minimum Spanning Tree Problem

This paper investigates the uncertain minimum spanning tree (UMST) problem where the edge weights are assumed to be uncertain variables. In order to propose effective solving methods for the UMST problem, path optimality conditions as well as some equivalent definitions for two commonly used types of UMST, namely, uncertain expected minimum spanning tree (expected UMST) and uncertain α-minimum spanning tree (α-UMST), are discussed. It is shown that both the expected UMST problem and the α-UMST problem can be transformed into an equivalent classical minimum spanning tree problem on a corresponding deterministic graph, which leads to effective algorithms with low computational complexity. Furthermore, the notion of uncertain most minimum spanning tree (most UMST) is initiated for an uncertain graph, and then the equivalent relationship between the α-UMST and the most UMST is proved. Numerical examples are presented as well for illustration.

scholarly journals The computational complexity of the κ-minimum spanning tree problem in graded matrices

1998 ◽  
Vol 36 (5) ◽  
pp. 61-67 ◽  
Author(s):  
T. Dudás ◽  
B. Klinz ◽  
G.J. Woeginger
Keyword(s):  
Computational Complexity ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Minimum Spanning Tree Problem

An Advanced AOI-Cast Algorithm Based on PCA

2014 ◽  
Vol 945-949 ◽  
pp. 2523-2526
Author(s):  
Yan Wang
Keyword(s):  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Undirected Graph ◽  
Gaussian Function ◽  
Fibonacci Sequence ◽  
Multicast Tree ◽  
Child Node ◽  
Efficient Manner ◽  
Simulation Results ◽  
Node Capacity

Voronoi-based Overlay Network (VON) has been proposed that promises to maintain high overlay topology consistency in a bandwidth-efficient manner. VoroCast constructs a spanning tree across all AOI neighbors based on Voronoi diagrams, while FiboCast dynamically adjusts the messaging range by a Fibonacci sequence. VoroCast improves the AOI scalability of P2P-based NVEs. However, one potential drawback of the schemes is that the child node degrees are only based on peers' positions, but not node capacities. Since each node in VoroCast has different capacity, packet loss will be unavoidable. VoroCast may lead to lower multicast efficiency in AOI. To these problems, an Advanced AOI-cast algorithm based on PCA is presented. In the algorithm, node capacity is related to node CPU (c), node bandwidth (b) and node memory (m), and the node capacity is calculated according to the PCA. An undirected graph is formed through all the nodes in the AOI and the edge weights are calculated by the Gaussian function. Through the prim algorithm, to generate the minimum spanning tree of the weighted undirected graph, and the minimum spanning tree is used as the final multicast tree. The message is delivered through the multicast tree. The simulation results show that the algorithm gains a greater improvement on multicast efficiency, and achieves better scalability.

scholarly journals About Some Localization Problems in Delaunay Triangulations

2015 ◽  
Vol 19 (6) ◽  
pp. 112-126
Author(s):  
N. F. Dyshkant
Keyword(s):  
Computational Complexity ◽  
Problem Solving ◽  
Computational Geometry ◽  
Delaunay Triangulation ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Computationally Efficient ◽  
Delaunay Triangulations ◽  
Efficient Approach ◽  
Geometric Search

We study some problems of nodes localization in a Delaunay triangulation and problem-solving procedures. For the problem of the set of nodes the computationally efficient approach that uses Euclidean minimum spanning tree of Delaunay triangulation is proposed. Efficient estimations for computational comlexity of the proposed methods in the average and in the worst cases are proved.computational geometry, geometric search, Delaunay triangulation, merging of overlapping triangulations, unregular discrete mesh, computational complexity

A new algorithm of generating a minimum spanning tree of a graph based on recursion

2014 ◽  
Author(s):  
Meiqin Pan ◽  
Zhijun Ding
Keyword(s):  
Spanning Tree ◽  
Minimum Spanning Tree
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