Networks of the Future

Architectures, Technologies, and Implementations

Mahmoud Elkhodr, Qusay F. Hassan, Seyed Shahrestani, Mahmoud Elkhodr, Qusay F. Hassan, Seyed Shahrestani

• Publisher: Chapman and Hall/CRC
• Year: 2017
• ISBN:
  9781351651561
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Engineering
• Index: Computer Science

With the ubiquitous diffusion of the IoT, Cloud Computing, 5G and other evolved wireless technologies into our daily lives, the world will see the Internet of the future expand ever more quickly. Driving the progress of communications and connectivity are mobile and wireless technologies, including traditional WLANs technologies and low, ultra-power, short and long-range technologies. These technologies facilitate the communication among the growing number of connected devices, leading to the generation of huge volumes of data. Processing and analysis of such "big data" brings about many opportunities, as well as many challenges, such as those relating to efficient power consumptions, security, privacy, management, and quality of service. This book is about the technologies, opportunities and challenges that can drive and shape the networks of the future.

Written by established international researchers and experts, Networks of the Future answers fundamental and pressing research challenges in the field, including architectural shifts, concepts, mitigation solutions and techniques, and key technologies in the areas of networking. The book starts with a discussion on Cognitive Radio (CR) technologies as promising solutions for improving spectrum utilization, and also highlights the advances in CR spectrum sensing techniques and resource management methods.

The second part of the book presents the latest developments and research in the areas of 5G technologies and Software Defined Networks (SDN). Solutions to the most pressing challenges facing the adoption of 5G technologies are also covered, and the new paradigm known as Fog Computing is examined in the context of 5G networks.

The focus next shifts to efficient solutions for future heterogeneous networks. It consists of a collection of chapters that discuss self-healing solutions, dealing with Network Virtualization, QoS in heterogeneous networks, and energy efficient techniques for Passive Optical Networks and Wireless Sensor Networks. Finally, the areas of IoT and Big Data are discussed, including the latest developments and future perspectives of Big Data and the IoT paradigms.

Part I

Cognitive Radio Networks

1 Cognitive Radio with Spectrum Sensing for Future Networks

Nabil Giweli, Seyed Shahrestani, and Hon Cheung

CONTENTS

1.1 Introduction and Background

1.2 Cognitive Radio Technology

1.2.1 Cognitive Radio Definition

1.3 Cognitive Radio Functions and Cycle

1.4 Cognitive Radio Challenges

1.4.1 Spectrum Sensing and White Space Determination: The Challenges

1.4.2 The Impact of Sensing Operations on the QoS of Applications

1.4.3 Spectrum Management Challenges

1.4.4 Spectrum Mobility Challenges

1.4.5 Spectrum Sharing Challenges

1.4.6 Other CR System Challenges

1.5 Sensing Methods

1.5.1 No Prior Information Required (Blind Sensing)

1.5.1.1 Energy Detection

1.5.1.2 Covariance-Based Detection

1.5.1.3 Based on Prior Information

1.5.1.4 Cyclostationary Feature Detection

1.5.1.5 Correlation Detection

1.5.1.6 Radio Identification–Based Sensing

1.5.1.7 Matched Filtering

1.5.1.8 Based on SU Cooperation

1.6 Factors Affecting the Selection of a Proper Sensing Method

1.6.1 CR Device Capability

1.6.2 QoS Required for Applications Running on the CR Device

1.6.3 A Priori Information

1.6.4 Level of Protection Required for the PU

1.6.5 CR Network Mode and Capability

1.7 CR Standardization Efforts with Sensing Consideration

1.8 Spectrum Sensing Implementation Issues in White-Fi Networks

1.8.1 Distinguishing a PU Transmission from SU Transmissions

1.8.2 Distinguishing White-Fi SUs from Other SUs

1.8.2.1 Pure White-Fi SUs

1.8.2.2 Heterogeneous SUs

1.8.3 Complications of Operational Adjustments

1.8.4 QoS Degradation

1.8.5 Selecting the Proper Sensing Method and Parameters

1.9 Conclusion

References

1.1 INTRODUCTION AND BACKGROUND

Traditionally, the radio frequency spectrum (RFS) is divided into frequency bands and regulated in most countries by their governments. Each country has a spectrum management process for allocating the frequency bands to licensed users based on technical and economic aspects. Although each country is independently allocating its RFS, governments regulate their RFSs in compliance with international and regional standards. In general, RFS bands are statically allocated as licensed or unlicensed. Each licensed band is strictly assigned to a licensed user. On the other hand, an unlicensed RFS band can be accessed and shared freely by anyone within some transmission constraints. Therefore, unlicensed bands are in high demand as many wireless technologies and radio devices are designed to work in these free RFS bands. The industrial, scientific, and medical (ISM) bands are well-known examples of unlicensed RFS bands that include the frequency bands 902–928 MHz, 2400–2483.5 MHz, and 5725–5850 MHz. These bands are widely used, especially by wireless devices and systems based on Wi-Fi and Bluetooth communication technologies, under several specific regulations regarding operational requirements, such as power transmission and antenna gain (Cui and Weiss, 2013). As a result of regulation for the RFS bands, many licensed RFS bands are underutilized, in terms of frequency, time, and location, as found by several surveys on RFS occupancy conducted in different regions around the world (Barnes et al., 2013; Palaios et al., 2013; SiXing et al., 2012). In the near future, current RFS regulations may not be able to handle the rapidly growing usage of wireless communication technologies for various applications with highly expected increases in transmission data rate requirements. Therefore, the wireless research community and RFS regulatory organizations face the challenges of achieving high utilization of the overall RFS and need to overcome capacity scarcity in high-demand frequency bands. To overcome these challenges, there will most likely be innovative wireless technologies and revisions of the RFS regulations (Masonta et al., 2013).

From a technical perspective, cognitive radio (CR) technology is a promising solution for achieving significant RFS utilization and to open a new opportunity in the sharing of the unlicensed RFS. The concept of CR was introduced in 1999 by Joseph Mitola (Mitola and Maguire, 1999). Under the CR concept, a radio device, called the CR device, enables an unlicensed user, called the secondary user (SU) or CR user, to use a licensed RFS band without harming the operation of the licensed user, also called the primary user (PU). A licensed band is generally underutilized by the primary user, as mentioned before. This underutilization usually results in the licensed band having possible unoccupied periods, called holes or white spaces (WSs). These unoccupied spectrum holes may be present based on time, frequency, and space. By the use of CR technology, SUs are able to opportunistically share access to these holes of the licensed bands, in addition to unrestricted access to the unlicensed bands. Therefore, more bandwidth is available to the increasing number of mobile and wireless users and the increasing number of internetworking applications.

The remainder of this chapter is organized as follows: the CR concept and its main functions are explained in Section 1.2. Several challenges facing each of the CR functions are discussed in Section 1.3. Different typ...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Contents
• Preface
• Acknowledgments
• Reviewers
• Editors
• Contributors
• Part I Cognitive Radio Networks
• Part II 5G Technologies and Software-Defined Networks
• Part III Efficient Solutions for Future Heterogenous Networks
• Part IV Big Data and the Internet of Things
• Index