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2020 | Book

Introduction Read chapter Read first chapter

Channel Aggregation and Fragmentation for Traffic Flows

Author: Dr. Lei Jiao

Publisher: Springer International Publishing

Book Series : SpringerBriefs in Electrical and Computer Engineering

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik"

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About this book

This book introduces the impact of channel aggregation (CA) and channel fragmentation (CF) on traffic flows, through analytical models, computer simulations, and test-bed implementations. Its content includes the concept of CA and CF, the basic concept and calculation of Markov chains (MCs), the modeling process of the CA and CF enabled system via MCs, the process of simulations, and a test-bed study based on a software defined radio.
This book can serve as a study guide for advanced-level students, who are interested in studying the impact of CA and CF techniques on traffic flows. This book would also interest communication engineers, who would like to learn MC modeling for performance evaluations, as it includes a step-by-step guidance for the modeling process via MCs, as well as its simulation approaches.

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
With the rapid development of modern communication systems and electronics technologies, spectrum utilization becomes more and more flexible and dynamic. Traditionally, a traffic flow is sent within one communication channel. With the help of channel aggregation (CA) technology, it is possible to adopt multiple channels for transmitting one flow, while the channel fragmentation (CF) technology can help divide one channel into multiple segments in order to transmit multiple flows. Studies on CA and CF and their relevant topics are numerous. To indicate the amount of the studies, we searched channel aggregation as the keyword in IEEE Xplore, on January 20th, 2019, and found 1256 relevant articles. In this chapter, we introduce the principle of CA and CF, and the concepts that are similar to them. We also provide an incomplete survey of these techniques with the main focus on cognitive radio networks (CRNs).
Lei Jiao
Chapter 2. Markov Chain and Stationary Distribution
Abstract
MC has been a valuable tool for analyzing the performance of complex stochastic systems since it was introduced by the Russian mathematician A. A. Markov (1856–1922) in the early 1900s. More and more system analyses have been carried out by using MC, including the analysis on CA and CF. In this chapter, we will briefly review the essential ingredients of MC that are necessary for the performance analysis presented in this book. A more comprehensive introduction of MC and its applications can be found in Nelson (2013, Probability, stochastic processes, and queueing theory: the mathematics of computer performance modeling).
Lei Jiao
Chapter 3. Markov Chain Analysis of CA and CF with a Single Type of Users
Abstract
In this chapter, we study the impact of CA and CF on traffic flows in the simplest system where there is one single type of flows generated by one single type of users. We will use CTMC to model the system, and the goal is to deliver the elementary concept of CTMC analysis for a system with CA and CF.
Lei Jiao
Chapter 4. Markov Chain Analysis of CA and CF with Multiple Types of Users
Abstract
In the previous chapter, we studied in depth the impact of CA and CF on traffic flows in systems where there exists only one type of users with one type of flows. In this chapter, the influence of CA and CF is studied in a more complicated scenario, i.e., the one in which multiple users exist, and where users have different priorities in using channel resources. CRN is a typical example for such a system, and we study the CRNs where PUs and SUs have one type of flows for each.
Lei Jiao
Chapter 5. Test-Bed Evaluation of CA and CF via a Software Defined Radio
Abstract
In Chaps. 3 and 4, we presented the analytical models and simulation approaches to study the impact of CA and CF on traffic flows in the single-flow single-user and the single-flow multi-user systems. In this chapter, we investigate the impact of CA and CF in a test-bed. We employ a software defined radio (SDR) from National Instruments (NI) to evaluate the performance of a CR system with user datagram protocol (UDP) flows. The adopted SDR is based on LTE protocol stack with additional functionalities that can support CA and CF, and accommodating PUs. By conducting measurements based on the test-bed system, we will be able to confirm that performance improvement can indeed be obtained by applying CA and CF in a real system.
Lei Jiao
Metadata
Title
Channel Aggregation and Fragmentation for Traffic Flows
Author
Dr. Lei Jiao
Copyright Year
2020
Electronic ISBN
978-3-030-33080-4
Print ISBN
978-3-030-33079-8
DOI
https://doi.org/10.1007/978-3-030-33080-4