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Course Detail

Course Name Markov Process and Queuing Theory
Course Code 19CCE331
Program B. Tech. in Computer and Communication Engineering
Year Taught 2019


Unit 1

Selected Topics in Probability and Random Variables -Memoryless property of exponential and geometric randomVariables -Moment generating function – Laplace-Stieljes transform (LST) of random variables -Selected Topics in Stochastic Processes –Stationarity –Ergodicity –Independence –Correlation – Stationary Increment and Independent Increment Processes – Bernoulli trials – Poisson processes – Gaussian processes.

Unit 2

Markov Processes -Discrete time Markov chains (DTMCs) -Continuous time Markov chains (CTMCs) – Recurrence –Transience –Stability – Renewal Processes and Markov Renewal Processes – Queueing Theory – Common queueing models (M/M/1, M/M/1/K, M/M/K/K, M/G/1, M/G/1/K, G/M/1, Geo/Geo/1, M/G/) – Vacation models -Loss networks and delay networks -Multiclass queueing models with priority -Open and closed networks of queues.

Unit 3

Discrete-Event Simulation of Queueing Systems – Applications to Telecommunications and Computer Communication Networks -Capacity design, Dynamic channel allocation in cellular networks and telecommunication switching -Throughput and delay analysis in wireless local area networks (WLANs) -Coverage analysis in wireless sensor networks (WSNs).


  • Vidyadhar G. Kulkarni, “Modeling and Analysis of Stochastic Systems”, CRC Press, 2016.
  • Anurag Kumar, “Discrete EventStochastic Processes”, available online


  • Dimitri P. Bertsekas, and Robert G. Gallager, “Data Networks”, PrenticeHall International, 1987.
  • Alberto Leon-Garcia, “Probability, Statistics, and Random Processes for Electrical Engineering”, 3rd ed. Pearson/Prentice Hall, 2008.

Evaluation Pattern

Assessment Internal External
Periodical 1 (P1) 15
Periodical 2 (P2) 15
*Continuous Assessment (CA) 20
End Semester 50
*CA – Can be Quizzes, Assignment, Projects, and Reports.

Objectives and Outcomes


  • To provide a thorough understanding of the mathematical foundations of telecommunication and computer communication networks
  • To teach the application of Markov processes and queueing theory to analyze the performance of and address the design questions in circuit- and packet-switching networks
  • To gain hands-on experience of discrete-event simulations of queueing systems

Course Outcomes

  • CO1: Able to map frequently occurring scenarios in telecommunication and computer networking into standard stochastic models, i.e., able to construct mathematical models from the physical description of the problems
  • CO2: Able to identify appropriate solution methods and physically interpret themathematical results
  • CO3: Able to analyze and compare the performance of queueing systems by discrete-event simulations
  • CO4: Gain professional knowledge and skills by term projects

CO – PO Mapping

CO1 3 3 3 3 3 3 3 3
CO2 3 3 3 3 3 3 2 3 3 3
CO3 3 3 3 3 3 3 2 3 3 3
CO4 3 3 2 3 2 3 3 3

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