Coordination of repeaters based on Simulated Annealing Algorithm and Monte-Carlo Algorithm

Authors:
Ya Cai;Yuanqing Xia;Li Dai;Chi Xiao;Jiaxiang Wu
Affiliations:
School of Automation, Beijing Institute of Technology, Beijing 100081, PR China;School of Automation, Beijing Institute of Technology, Beijing 100081, PR China;School of Automation, Beijing Institute of Technology, Beijing 100081, PR China;School of Automation, Beijing Institute of Technology, Beijing 100081, PR China;School of Automation, Beijing Institute of Technology, Beijing 100081, PR China
Venue:
Neurocomputing
Year:
2012

Citing 7
Cited 1

Optimal solution of set covering/partitioning problems using dual heuristics

Management Science
Optimizing a Ring-Based Private Line Telecommunication Network Using Tabu Search

Management Science
A GRASP algorithm to solve the unicost set covering problem

Computers and Operations Research
Brief paper: Stabilization of Markovian jump linear system over networks with random communication delay

Automatica (Journal of IFAC)
Letter: An efficient simulated annealing algorithm for the minimum vertex cover problem

Neurocomputing
Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm

Neurocomputing
MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections

IEEE Transactions on Neural Networks

A simulated annealing algorithm for sparse recovery by l0 minimization

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper considers the design of repeaters network in both flat and mountainous area. The main purpose is to design a network that is not only able to accommodate the communication need of all users in a certain area, but also with lower cost than previous design methods of the network. Considering the propagation and attenuation of radio signals, the problem can be transformed to search for best locations of centers of circles in order to reach maximum coverage percent in the whole area. Applying the Simulated Annealing Algorithm and Monte-Carlo Algorithm, numerical examples are given to show the potential of the proposed techniques. Compared with previous method, our proposed method shows its effectiveness.