An Adaptive Uniform Fractional Guard Channel Algorithm: A Learning Automata Approach

2003 ◽  
pp. 405-409 ◽  
Author(s):  
Hamid Beigy ◽  
M. R. Meybodi
Keyword(s):  
Learning Automata ◽  
Guard Channel

scholarly journals ADAPTIVE LIMITED FRACTIONAL GUARD CHANNEL ALGORITHMS: A LEARNING AUTOMATA APPROACH

2009 ◽  
Vol 17 (06) ◽  
pp. 881-913 ◽  
Author(s):  
HAMID BEIGY ◽  
M. R. MEYBODI
Keyword(s):  
Prior Knowledge ◽  
Computer Simulations ◽  
Mobile Networks ◽  
Blocking Probability ◽  
Learning Automata ◽  
Dropping Probability ◽  
Guard Channel ◽  
Simulation Results ◽  
Cellular Mobile

In this paper, two learning automata based adaptive limited fractional guard channel algorithms for cellular mobile networks are proposed. These algorithms try to minimize the blocking probability of new calls subject to the constraint on the dropping probability of the handoff calls. To evaluate the proposed algorithms, computer simulations are conducted. The simulation results show that the performance of the proposed algorithms are close to the performance of the limited fractional guard channel algorithm for which prior knowledge about traffic parameters are needed. The simulation results also show that the proposed algorithms outperforms the recently introduced dynamic guard channel algorithms.

scholarly journals A learning automata-based adaptive uniform fractional guard channel algorithm

2014 ◽  
Vol 71 (3) ◽  
pp. 871-893 ◽  
Author(s):  
Hamid Beigy ◽  
M. R. Meybodi
Keyword(s):  
Learning Automata ◽  
Guard Channel

scholarly journals Learning automata based dynamic guard channel algorithms

2011 ◽  
Vol 37 (4) ◽  
pp. 601-613 ◽  
Author(s):  
Hamid Beigy ◽  
M.R. Meybodi
Keyword(s):  
Learning Automata ◽  
Guard Channel

SIG-CLA: A Significant Community Detection based on Cellular Learning Automata

2020 ◽  
Author(s):  
Mohammad Mehdi Daliri Khomami ◽  
Alireza Rezvanian ◽  
Ali Mohammad Saghiri ◽  
Mohammad Reza Meybodi
Keyword(s):  
Community Detection ◽  
Learning Automata ◽  
Cellular Learning Automata

Deterministic estimator learning automata-based neighbor discovery schemes for D2D networks with directional antennas

2021 ◽  
pp. 101329
Author(s):  
Weidang Lu ◽  
Lixia Weng ◽  
Chenkai Li ◽  
Guoxing Huang ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Learning Automata ◽  
Directional Antennas ◽  
Neighbor Discovery

scholarly journals Finding Maximum Clique in Stochastic Graphs Using Distributed Learning Automata

2015 ◽  
Vol 23 (01) ◽  
pp. 1-31 ◽  
Author(s):  
Alireza Rezvanian ◽  
Mohammad Reza Meybodi
Keyword(s):  
Community Structure ◽  
Dominating Set ◽  
Learning Automata ◽  
Random Variables ◽  
Graph Model ◽  
Maximum Clique ◽  
Distribution Functions ◽  
Maximum Clique Problem ◽  
Stochastic Graphs ◽  
Stochastic Graph

Because of unpredictable, uncertain and time-varying nature of real networks it seems that stochastic graphs, in which weights associated to the edges are random variables, may be a better candidate as a graph model for real world networks. Once the graph model is chosen to be a stochastic graph, every feature of the graph such as path, clique, spanning tree and dominating set, to mention a few, should be treated as a stochastic feature. For example, choosing stochastic graph as the graph model of an online social network and defining community structure in terms of clique, and the associations among the individuals within the community as random variables, the concept of stochastic clique may be used to study community structure properties. In this paper maximum clique in stochastic graph is first defined and then several learning automata-based algorithms are proposed for solving maximum clique problem in stochastic graph where the probability distribution functions of the weights associated with the edges of the graph are unknown. It is shown that by a proper choice of the parameters of the proposed algorithms, one can make the probability of finding maximum clique in stochastic graph as close to unity as possible. Experimental results show that the proposed algorithms significantly reduce the number of samples needed to be taken from the edges of the stochastic graph as compared to the number of samples needed by standard sampling method at a given confidence level.

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