Stochastic Modeling In Telecommunications With Python(1st Edition)

Book details

Book Price $0 : Unlock The Power Of Stochastic Modeling To Transform Your Telecommunications Systems With This Comprehensive Guide. Dive Deep Into The World Of Probability And Advanced Mathematical Concepts As Applied To The Ever-evolving Landscape Of Telecom Networks. Whether You're An Engineer, A Researcher, Or A Student, This Book Will Equip You With The Tools And Insights Needed To Harness The Potential Of Stochastic Processes, Ensuring Robust, Efficient, And Cutting-edge Telecommunications Solutions.Key Features:- Comprehensive Coverage: Delves Into A Wide Array Of Stochastic Models, Covering Everything From Basic Probability Theory To Advanced Network Calculus.- Practical Python Code: Each Chapter Includes Python Scripts To Facilitate Hands-on Learning And Practical Application Of The Concepts Discussed.- Real-World Applications: Learn How To Apply Stochastic Modeling To Real-world Telecommunication Challenges, Enhancing Your Problem-solving Capabilities.- Step-by-Step Explanations: Each Topic Is Meticulously Detailed With Clear, Step-by-step Explanations To Ensure Understanding, Regardless Of Your Prior Knowledge.What You Will Learn:- Understand Foundational Stochastic Processes, Including Markov Chains And Poisson Processes, And Their Critical Role In Telecommunications.- Master Essential Probability Theories Such As Density Functions And Expectation Values.- Explore The Intricacies Of Poisson Processes For Modeling Network Traffic.- Dive Into Discrete And Continuous-time Markov Chains For Network State Analysis.- Utilize Renewal Process Models To Manage Repetitive Communication Events.- Grasp The Basics Of Queueing Theory, Focusing On M/M/1 And M/M/c Systems.- Analyze Complex Queueing Models Like M/G/1 And Priority Queues.- Apply Erlang's B And C Formulas In Telecommunication Scenarios.- Calculate Traffic Intensity And Utilization Factors For Network Optimization.- Solve Stochastic Differential Equations For Telecom Signal Behavior Modeling.- Model State Transitions Using Birth-death Process Differential Equations.- Evaluate Network Component Reliability With Exponential And Weibull Distributions.- Predict Network Paths Using Random Walk Theories.- Analyze Packet Loss And Delay With Stochastic Network Modeling.- Study The Performance Of Open And Closed Queue Networks.- Explore Gaussian Processes For Signal Processing And Noise Modeling.- Implement Hidden Markov Models In Voice Recognition Technologies.- Model Network Jumps With Lévy Processes.- Enhance Network Performance With Stochastic Control Strategies. See more