Research Article
Review of Markov Chain and Its Applications in Telecommunication Systems
@INPROCEEDINGS{10.1007/978-3-031-06374-9_24, author={Amel Salem Omer and Dereje Hailemariam Woldegebreal}, title={Review of Markov Chain and Its Applications in Telecommunication Systems}, proceedings={e-Infrastructure and e-Services for Developing Countries. 13th EAI International Conference, AFRICOMM 2021, Zanzibar, Tanzania, December 1-3, 2021, Proceedings}, proceedings_a={AFRICOMM}, year={2022}, month={5}, keywords={Hidden Markov model Initial distribution Markov chain Modeling Prediction State Steady state distribution Transition probability}, doi={10.1007/978-3-031-06374-9_24} }- Amel Salem Omer
Dereje Hailemariam Woldegebreal
Year: 2022
Review of Markov Chain and Its Applications in Telecommunication Systems
AFRICOMM
Springer
DOI: 10.1007/978-3-031-06374-9_24
Abstract
Markov chain is a powerful mathematical tool that is used to predict future state of a random process based on its present state, for classical or first-order Markov chain, and past states for higher-order Markov chain. Markov chain has a wide range of applications in various fields of science and technology. To mention some in the area of telecommunication systems: Internet page ranking; Internet traffic modeling; language source modeling in natural language processing for text compression and text generation applications; speech recognition; wireless channel modeling; spectrum occupancy prediction for cognitive radio; user mobility modeling, handover management, and operation status monitoring in cellular mobile networks; network service and maintenance optimization; and in Markov chain Monte Carlo simulation methods. The main objective of this paper is to review fundamental concepts in Markov chain for discrete sources with emphasis on its application in telecommunication systems. It introduces terminologies, method to compute transition probabilities from real data, computing different states of the chain, and possible applications areas. The focus is given for both classical and higher-order Markov chain as well as Hidden Markov models. While acknowledging a related lecture note published in 2010, which we came to know lately, this review includes additional topics, such as higher-order Markov chain and Hidden Markov models, and tries to present core ideas in less mathematically rigorous but more practicable way. We hope that interested researchers who wish to apply Markov chain for various applications will benefit from this review.
