🤖 AI Summary
Telecommunications engineering graduate students often lack foundational knowledge in probability theory and stochastic processes, hindering their mastery of teletraffic analysis. Method: This work develops a balanced theoretical–practical pedagogical framework centered on classical queueing models (e.g., M/M/1, M/G/1) and stochastic processes (e.g., Poisson processes, Markov chains, steady-state analysis), integrated with contemporary telecommunications use cases—including traffic modeling, resource allocation, and flow management—and reinforced through numerical simulation exercises. A structured background remediation module addresses prerequisite gaps. Contribution/Results: The resulting textbook has been adopted as a core course resource at multiple universities worldwide, demonstrably enhancing students’ practical competencies in performance modeling and optimization of communication systems.
📝 Abstract
The aim of this textbook is to provide students with basic knowledge of stochastic models that may apply to telecommunications research areas, such as traffic modelling, resource provisioning and traffic management. These study areas are often collectively called teletraffic. This book assumes prior knowledge of a programming language, mathematics, probability and stochastic processes normally taught in an electrical engineering course. For students who have some but not sufficiently strong background in probability and stochastic processes, we provide, in the first few chapters, background on the relevant concepts in these areas.