scholarly journals Markov chain model of HomePlug CSMA MAC for determining optimal fixed contention window size

2011 ◽  
Author(s):  
Evan Kriminger ◽  
Haniph Latchman
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Window Size ◽  
Contention Window ◽  
Chain Model ◽  
Contention Window Size

Several Contention Window Adjustment Techniques for Improving Unsaturated throughput of Wireless LANs

2014 ◽  
Vol 931-932 ◽  
pp. 952-956
Author(s):  
Jesada Sartthong ◽  
Suvepon Sittichivapak ◽  
Nitthita Chirdchoo
Keyword(s):  
Load Condition ◽  
Window Size ◽  
Traffic Load ◽  
Contention Window ◽  
Area Network ◽  
Chain Model ◽  
Function Concept ◽  
Contention Window Size ◽  
Comparison Results ◽  
Size Adjustment

This paper proposes the several contention window adjustment schemes in backoff process as well-known backoff algorithm (BA) for improving the performance of wireless local area network (WLAN). In addition, this research introduces a new unsaturated discrete Markov chain model in fixed backoff stages and fixed contention window sizes technique (FBFC). The proposed contention window adjustment schemes are designed by applying the moment generating function concept in random variable and process theorem. Unsaturated throughput parameters are used to compare the performance of all contention window size adjustment techniques based on IEEE802.11b WLAN standards. The comparison results show that Bernoulli and Double adjustment schemes are good contention window size adjustments at light traffic load, and the Even contention window size adjustment operates well at high traffic load condition.

scholarly journals The performance analysis of direct/cooperative transmission to support QoS in WLANs

2014 ◽  
Vol 11 (3) ◽  
pp. 1143-1156 ◽  
Author(s):  
Chien-Erh Weng ◽  
Jyh-Horng Wen ◽  
Hsing-Chung Chen ◽  
Lie Yang
Keyword(s):  
Cooperative Communications ◽  
Markov Chain Model ◽  
Window Size ◽  
Cooperative Transmission ◽  
Chain Model ◽  
The Past ◽  
Contention Window Size ◽  
Throughput And Delay ◽  
Significant Attention ◽  
Better Than

In the past decades, cooperative communications schemes have gained significant attention in wireless networks. The cooperative scheme leads to longer transmission time which can considerably degrade the system performance. We evaluate the saturation throughput and saturation delay of the Markov chain model with direct/cooperative schemes to support QoS in WLANs. Simulation results show that differentiating the contention window size is better than differentiating the arbitration interframe space in terms of throughput and delay.

An Efficient Markov Chain Model for the Simulation of Heterogeneous Soil Structure

2004 ◽  
Vol 68 (2) ◽  
pp. 346 ◽  
Author(s):  
Keijan Wu ◽  
Naoise Nunan ◽  
John W. Crawford ◽  
Iain M. Young ◽  
Karl Ritz
Keyword(s):  
Markov Chain ◽  
Soil Structure ◽  
Markov Chain Model ◽  
Chain Model ◽  
Heterogeneous Soil

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

Evaluating basketball player’s rotation line-ups performance via statistical markov chain modelling

2021 ◽  
pp. 174795412110090
Author(s):  
Pavlos Kolias ◽  
Nikolaos Stavropoulos ◽  
Alexandra Papadopoulou ◽  
Theodoros Kostakidis
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Specific Rotation ◽  
Multiple Regression Model ◽  
Chain Model ◽  
Know How ◽  
Basketball Game ◽  
Long Run ◽  
Rotation Line ◽  
Rating Category

Coaches in basketball often need to know how specific rotation line-ups perform in either offense or defense and choose the most efficient formation, according to their specific needs. In this research, a sample of 1131 ball possession phases of Greek Basket League was utilized, in order to estimate the offensive and defensive performance of each formation. Offensive and defensive ratings for each formation were calculated as a function of points scored or received, respectively, over possessions, where possessions were estimated using a multiple regression model. Furthermore, a Markov chain model was implemented to estimate the probabilities of the associated formation’s performance in the long run. The model could allow us to distinguish between overperforming and underperforming formations and revealed the probabilities over the evolution of the game, for each formation to be in a specific rating category. The results indicated that the most dominant formation, in terms of offense, is Point Guard-Point Guard-Small Forward-Power Forward-Center, while defensively schema Point Guard-Shooting Guard-Small Forward-Center-Center had the highest rating. Such results provide information, which could operate as a supplementary tool for the coach’s decisions, related to which rotation line-up patterns are mostly suitable during a basketball game.

An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

2021 ◽  
pp. 1-11
Author(s):  
Yuan Zou ◽  
Daoli Yang ◽  
Yuchen Pan
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Historical Data ◽  
Markov Chain Model ◽  
Back Propagation ◽  
The United States ◽  
Domestic Product ◽  
Chain Model

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

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