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Series Editor: Biswanath Mukherjee Optical Networks

Massimo Tornatore Gee-Kung Chang Georgios Ellinas Editors

Fiber-Wireless Convergence in Next-Generation Communication Networks Systems, Architectures, and Management

Optical Networks

Series editor

Biswanath Mukherjee, Davis, USA

The book series in Optical Networks encompasses both optical communications and networks, including both theoretical and applied books. The series describes current advances at the cutting edge of the field and is aimed especially at industry practitioners, researchers, and doctoral students.

The series emphasizes the following major areas:

• optical network architectures; • enabling technologies for optical communication networks; • wavelength-division multiplexing (WDM) based networks; • optical access and metro networks; • long-haul optical networks; • optical packet networks; • optical burst-switched networks; • fault-tolerant optical networks; • optical network control and management; and • emerging industry standards.

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• Intermediate to advanced level textbooks as well as expository books that extend and unify our knowledge and understanding of particular areas;

• Handbooks and professional reference works that redefine “state-of-the-art,” emphasizing expository surveys, completely new advances, and combinations thereof;

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• Research monographs that make substantial contributions to new knowledge in specialty subtopics.

These peer-reviewed titles ensure the highest quality content.

More information about this series at http://www.springer.com/series/6976

Massimo Tornatore • Gee-Kung Chang Georgios Ellinas Editors

Fiber-Wireless Convergence in Next-Generation Communication Networks Systems, Architectures, and Management

123

Editors Massimo Tornatore Department of Electronics, Information and Bioengineering, DEIB

Politecnico di Milano Milan Italy

Gee-Kung Chang School of Electrical and Computer Engineering

Georgia Institute of Technology Atlanta, GA USA

Georgios Ellinas Department of Electrical and Computer Engineering

University of Cyprus Cyprus Cyprus

ISSN 1935-3839 ISSN 1935-3847 (electronic) Optical Networks ISBN 978-3-319-42820-8 ISBN 978-3-319-42822-2 (eBook) DOI 10.1007/978-3-319-42822-2

Library of Congress Control Number: 2016954914

© Springer International Publishing Switzerland 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made.

Printed on acid-free paper

This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Gee-Kung Chang dedicates this book to his wife Sharon

Georgios Ellinas dedicates this book to his nephew Nicolas and his niece Carina

Massimo Tornatore dedicates this book to his father Antonio

Foreword

Optical fiber networks and wireless networks have recently undergone significant technological and architectural evolution through, e.g., massive deployments of 4G mobile networks and passive optical networks (PON). These sustained infrastruc- ture upgrades have led to enormous investments for operators (billions of dollars). The next important step is near, as 5G communication networks are expected to be deployed by 2020, featuring unprecedented performance in terms of higher data rates, lower latency, and network flexibility. To achieve this, 5G will resort to solutions such as small-cell deployment (micro, femto, etc.), coordinated multi-cell processing, and centralized radio access networks that will ultimately burden the optical metro/access segment due to the massive amount of mobile traffic to be backhauled with sub-ms latency. Operators are now looking at a promising set of techniques characterized by strict cooperation between fiber-based and wireless-based technologies. These techniques are generically referred to as fiber- wireless convergence, forming the main subject of this book.

This book’s editors have done an excellent job in capturing the various technical facets of fiber-wireless convergence. They present, using a set of clear and cohesive edited contributions, the impact and role of fiber-wireless convergence in various areas of network engineering, covering transmission systems, network architec- tures, and network management and control.

The book has been co-edited by Professor Gee-Kung Chang of Georgia Tech University in Atlanta, Professor George Ellinas of University of Cyprus, and Professor Massimo Tornatore of Politecnico di Milano, Italy. The editorial team has diverse and complementary expertise on the various areas of fiber-wireless con- vergence, which is then reflected in the comprehensive coverage of the book that spans, e.g., from detailed description of key transmission systems such as digital radio-over-fiber (D-RoF), to the role of software-defined network (SDN) control for convergence.

The book is divided into four parts: (1) path towards convergence; (2) systems; (3) architectures; and (4) management. In Part I, the reader is introduced to fiber-wireless convergence, both from the point of view of today’s market trends

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and from the point of view of the 5G technical challenges that call for convergence. In Part II, transmission systems which are more directly affected by convergence are overviewed, with a description of analog and D-RoF techniques, millimeter-wave wireless, and a detailed overview of system challenges that can be addressed by SDN. Part III captures the relevant problems and challenges on network architec- tures, covering topics as PON-based convergence, hoteling of remotized baseband functions “BBU hoteling”, and the “No Cell” vision of future mobile networks. Finally, Part IV includes relevant management and control problems such as the new role of metropolitan central office (Next Generation Point of Presence), and impact of SDN and radio coordination on convergence.

The editors deserve praise for the excellent lineup of contributing authors, who come from leading companies, reputable universities, and research laboratories, and with strong geographical diversity. This book is highly recommended as it offers timely, comprehensive, and authoritative reference to information on fiber-wireless convergence. We expect that the reader will enjoy the book.

August 2016 Biswanath Mukherjee University of California

Davis, USA

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Preface

Communication networks must continuously evolve to ensure a sustainable growth of our “Internet Society.” It has been repeatedly observed that the push for more and more advanced network services leads inevitably to an exponential growth of traffic volumes and to higher quality-of-service requirements by the users; only by resorting to novel technologies and architectural solutions network, operators can keep pace with users’ requirements. The next big innovations in the telecom industry seem to be the forecasted massive deployment of IoT devices (hence the related machine-to-machine communication paradigm), the explosion of video-content network distribution, and the development of ultra-low latency net- work services. To address the technical challenges associated with these services, many companies, research institutes, and standardization bodies have now started the race towards the 5th generation of mobile communications. Fifth-generation networks are expected to support unprecedented bit rates, guaranteeing strict latency and reliable performance, and offering support for connecting together a tremendous number of devices. Fifth-generation networks will use very dense, low-power, small-cell networks with a high spatial reuse and a high degree of coordination due to strong inter-cell interference. Both fiber-based and wireless- based backhaul solutions will be used to connect small cells and the core network, but so far, access and backhaul are individually designed and therefore not jointly optimized. Hence, the design of 5G networks has long dictated the necessity to merge the currently distinct fiber and wireless infrastructures into an amalgamated network capable of combining the strength of both technologies: the stability and high bandwidth of optical fibers with the flexibility and mobility of wireless net- works. This process of integration of the two technologies is usually referred as fiber-wireless convergence (or fixed-wireless convergence) and comprises a large set of technical challenges and solutions.

In this book, we provide the recent developments in the field of fiber-wireless convergence, concentrating on solutions that will be used to support the backhaul, midhaul, and fronthaul of 5G networks. The text presents the trends of industry, as well as current research, in state-of-the-art architectures of converged systems and networks, and takes a vertically layered approach starting from systems,

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to architectures, to management/control issues of fixed-mobile convergence. This book is different from a number of other works on 5G networks that tend to focus heavily on the wireless aspects of 5G. Instead, we decided to look at both networking and systems issues, and focus on the latest research developments in a number of areas including radio over fiber, centralized cloud radio access network, and coordinated multi-point transmission for multiple base stations. This book is meant to be an introduction for any reader interested in having a holistic approach to the technical issues in fiber-wireless convergence and to readers interested in understanding some key aspects in more depth. The aim of the editors is to present a body of work that can provide the research scientist, company engineer, and the university professor/researcher with a better understanding on fiber-wireless con- vergence and ensure that experienced as well as novice researchers can have a single handy source of reference on this topic.

The book is divided into four parts that can appeal to different needs of readers, who are interested in various networking domains and issues. Part I is comprised of the introduction, the market, and the technical motivations for fiber-wireless convergence. Part II presents and discusses transmission systems for wireless-signal transport over fiber (A-RoF and D-RoF), and competing technology to these sys- tems (namely multi-band RF and millimeter-wave transmission), and a set of opportunities that software-defined networks (SDNs) enable for such transmis- sion systems. Part III concentrates on architectural issues related to network inte- gration of fiber and wireless technologies (including use of PONs for mobile backhauling, baseband-unit hoteling, and centralized/coordinated architectures for radio access network, as the No-More-Cell architecture). Finally, Part IV covers management/control topics related to how and which network functions should converge in specific metro offices (Next Generation Point of Presence), as well as provides a closer look to some of these functions such as radio coordination and other SDN-controlled cloud services.

Acknowledgments We are extremely grateful to our past advisors and mentors, colleagues, students, and friends, who have motivated, inspired, and guided us to work in the new field of fiber-wireless convergence. All of them offered us their invaluable advise, exceptional insight, and foresight. They provided us with valu- able guidance throughout the years and helped us better understand and appreciate various aspects of this new technological area. We would also be remiss if we did not extend a thank you to Professor Biswanath Mukherjee for taking the time to write the Foreword for this book and for his patience and encouragement while this book was being prepared and delivered. We also wish to express our thanks to Zoe Kennedy, Mary James, and the entire publishing team at Springer Verlag, for their effort and patience in order to bring this project to fruition. Also, a special thanks goes to all the authors who contributed chapters for this book. Finally, Georgios Ellinas is greatly indebted to his family for their understanding and patience during this undertaking. Gee-Kung Chang wishes to thank his wife for her unwavering support and express profound gratitude to Ken Byers who endows an eminent scholar chair professor in advanced telecommunications research at Georgia Tech for several decades. Massimo Tornatore gratefully acknowledges his family and his

x Preface

fiancée Angela, for their constant love and support. Massimo Tornatore wants to acknowledge the European Community’s COMBO project, during which he gained an invaluable amount of knowledge on the topics of this book.

Milan, Italy Massimo Tornatore Associate Professor

Atlanta, USA Gee-Kung Chang Professor

Byers Eminent Scholar Chair Professor in Optical Networking

Georgia Research Alliance Eminent Scholar

Cyprus, Cyprus Georgios Ellinas Associate Professor

Preface xi

Contents

Part I The Path Towards Convergence

1 Future Radio Access, Wi-Fi-LTE, LTE-Advanced: The Path to 5G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Rajarajan Sivaraj and Prasant Mohapatra 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.1.1 LTE Principles of Operation and Deployment . . . . . . . 4 1.2 Carrier Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.2.1 Definitions and Terminologies . . . . . . . . . . . . . . . . . . . 9 1.2.2 Types of Carrier Aggregation . . . . . . . . . . . . . . . . . . . 10 1.2.3 Radio Resource Management Framework for CA . . . . 15

1.3 Transmission Diversity and Spatial Multiplexing . . . . . . . . . . . . 18 1.3.1 Transmit Diversity—Definition and Terminologies . . . 18 1.3.2 MIMO and Spatial Multiplexing—Definition and

Terminologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.3.3 Coordinated Multi-point Transmission. . . . . . . . . . . . . 19 1.3.4 Types of CoMP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.3.5 Advancements: 3D Beamforming . . . . . . . . . . . . . . . . 24 1.3.6 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

1.4 Wi-Fi-LTE, Unlicensed LTE . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 1.4.1 Definition and Terminologies . . . . . . . . . . . . . . . . . . . 27 1.4.2 CA of LTE-Licensed and LTE-U CCs . . . . . . . . . . . . 28

1.5 Network Heterogeneity: Self-organizing HetNets . . . . . . . . . . . . 30 1.5.1 Definition and Terminologies . . . . . . . . . . . . . . . . . . . 30 1.5.2 Background on Inter-cell Interference Coordination

(ICIC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 1.5.3 Enhanced Inter-cell Interference Coordination

(EICIC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

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1.5.4 Defining the CRE Region . . . . . . . . . . . . . . . . . . . . . . 35 1.5.5 Enhancements: eICIC with CA . . . . . . . . . . . . . . . . . . 37

1.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2 Evolution and Trends of Broadband Access Technologies and Fiber-Wireless Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Yiran Ma and Zhensheng Jia 2.1 Traffic Trend. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.2 Technologies of Broadband Access Networks . . . . . . . . . . . . . . 45

2.2.1 Broadband Wireline Access Networks. . . . . . . . . . . . . 45 2.2.2 Broadband Wireless Access Networks. . . . . . . . . . . . . 60

2.3 Fiber-Wireless Convergence and Technology Evolution . . . . . . . 69 2.3.1 Fiber-Based Distributed Antenna Systems (DASs) . . . 69 2.3.2 Ultra-High-Speed Fiber-Wireless Transmission . . . . . . 70 2.3.3 Fiber-Wireless for Backhaul and the Fronthaul

of HetNet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 2.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

3 The Benefits of Convergence Through Fiber-Wireless Integration and Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Gee-Kung Chang and Lin Cheng 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 3.2 Convergence of Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

3.2.1 Centralization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.2.2 Resource Sharing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

3.3 Convergence of Links. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 3.3.1 Mobile Backhaul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 3.3.2 Mobile Midhaul and Fronthaul . . . . . . . . . . . . . . . . . . 87

3.4 Convergence of Bands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 3.4.1 All-Band Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 3.4.2 MMW Links . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Part II Novel Systems/Subsystems for Fi-Wi Networks

4 Analog and Digitized Radio-over-Fiber . . . . . . . . . . . . . . . . . . . . . . . 99 Maurice Gagnaire 4.1 Existing Radio Cellular Networks. . . . . . . . . . . . . . . . . . . . . . . . 100 4.2 A-RoF Versus Baseband-over-Fiber . . . . . . . . . . . . . . . . . . . . . . 102

4.2.1 Option 1: RF-Modulated Signals . . . . . . . . . . . . . . . . . 103 4.2.2 Option 2: IF Modulated Signals . . . . . . . . . . . . . . . . . 103 4.2.3 Option 3: Baseband-over-Fiber . . . . . . . . . . . . . . . . . . 104 4.2.4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

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4.3 Transmission of Microwave Signals on Optical Fibers . . . . . . . . 104 4.3.1 Intensity Modulation (IM) and Direct Detection

(DD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 4.3.2 External Modulation and Direct Detection

(EM-DD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.3.3 Photo-detector-Based Heterodyning (HE) with

Direct Detection (HE-DD) . . . . . . . . . . . . . . . . . . . . . . 107 4.3.4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

4.4 Analog Radio-over-Fiber (A-RoF) . . . . . . . . . . . . . . . . . . . . . . . 109 4.4.1 A-RoF for “RF-over-Fiber” . . . . . . . . . . . . . . . . . . . . . 110 4.4.2 A-RoF for “IF-over-Fiber”. . . . . . . . . . . . . . . . . . . . . . 111 4.4.3 A-RoF for Multi-antennas Sites by Means

of Sub-carrier Multiplexing (SCM) . . . . . . . . . . . . . . . 112 4.4.4 A-RoF for Multi-antennas Sites by Means

of Wavelength-Division Multiplexing (WDM) . . . . . . 114 4.5 Digitized Radio-over-Fiber (D-RoF) . . . . . . . . . . . . . . . . . . . . . . 117

4.5.1 Band-pass Sampling Theory . . . . . . . . . . . . . . . . . . . . 118 4.5.2 D-RoF for a Single-Antenna Site. . . . . . . . . . . . . . . . . 120 4.5.3 D-RoF for a Multiple-Antenna Site . . . . . . . . . . . . . . . 122

4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

5 Overview of Standardization for D-RoF . . . . . . . . . . . . . . . . . . . . . . 127 Silvano Frigerio, Alberto Lometti and Vincenzo Sestito 5.1 CPRI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

5.1.1 Specification Overview . . . . . . . . . . . . . . . . . . . . . . . . 129 5.1.2 System Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 5.1.3 Main Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 5.1.4 Interface Description . . . . . . . . . . . . . . . . . . . . . . . . . . 131 5.1.5 CPRI Compression and CPRI Throughput

Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 5.2 OBSAI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

5.2.1 OBSAI Specifications Status . . . . . . . . . . . . . . . . . . . . 139 5.2.2 System Architecture Overview . . . . . . . . . . . . . . . . . . 139 5.2.3 RP3-01 Insight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 5.2.4 CPRI Versus OBSAI RP3-01 . . . . . . . . . . . . . . . . . . . 145

5.3 D-RoF Transport Over Optical Networks . . . . . . . . . . . . . . . . . . 146 5.3.1 CPRI Over OTN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 5.3.2 Viable Network Applications for CPRI Over

WDM/OTN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 5.4 ORI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

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6 Wireless Delivery of over 100 Gb/s mm-Wave Signal in the W-band . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Jianjun Yu 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 6.2 Approaches for the Realization of Large Capacity

(>100 Gb/s) Fiber Wireless Integration System . . . . . . . . . . . . . 160 6.2.1 Optical PDM Combined with MIMO Reception . . . . . 161 6.2.2 Advanced Multi-level Modulation . . . . . . . . . . . . . . . . 166 6.2.3 Optical Multi-carrier Modulation . . . . . . . . . . . . . . . . . 169 6.2.4 Electrical Multi-carrier Modulation . . . . . . . . . . . . . . . 173 6.2.5 Antenna Polarization Multiplexing . . . . . . . . . . . . . . . 175 6.2.6 Multi-band Multiplexing . . . . . . . . . . . . . . . . . . . . . . . 178

6.3 Problems Existing in the Large Capacity Fiber Wireless Integration System and Corresponding Solutions . . . . . . . . . . . . 182 6.3.1 Wireless Multi-path Effects Due to Different

Wireless Transmission Distances . . . . . . . . . . . . . . . . . 182 6.3.2 Advance Algorithms Based on DSP . . . . . . . . . . . . . . 184

6.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185

7 Systems Challenges for SDN in Fiber Wireless Networks . . . . . . . . 189 Neda Cvijetic and Ting Wang 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 7.2 System-Level Fiber Wireless Network Challenges . . . . . . . . . . . 192

7.2.1 Signaling Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 7.2.2 Network Densification . . . . . . . . . . . . . . . . . . . . . . . . . 194 7.2.3 Network Topology. . . . . . . . . . . . . . . . . . . . . . . . . . . . 195

7.3 SDN-Based Control Plane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 7.3.1 SDN-Based Control in Fiber Wireless Networks . . . . . 198

7.4 Recent Progress in SDN for Fiber Wireless Networks . . . . . . . . 201 7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

Part III Novel Network Architectures for Fi-Wi Networks

8 Architectural Evolution and Novel Design of Fiber-Wireless Access Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Cheng Liu 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 8.2 Overview of Existing Fiber-Wireless Access Architectures. . . . . 215

8.2.1 Macrocell and Small Cell with Fiber-Optic Backhaul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215

8.2.2 Distributed Antenna System . . . . . . . . . . . . . . . . . . . . 219 8.2.3 Cloud Radio Access Network (C-RAN) . . . . . . . . . . . 221

8.3 Novel Cloud Radio-Over-Fiber Access Architecture. . . . . . . . . . 224

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8.3.1 Generic Cloud-RoF Architecture and Operational Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224

8.3.2 Reconfigurable Cloud-RoF Architecture with WDM Techniques . . . . . . . . . . . . . . . . . . . . . . . . 226

8.3.3 Multi-Service Delivery Including Future- Proof Millimeter-Wave Services . . . . . . . . . . . . . . . . . 228

8.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232

9 Advanced Architectures for PON Supporting Fi-Wi Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Georgios Ellinas, Kyriakos Vlachos, Chrysovalanto Christodoulou and Mohamed Ali 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 9.2 Backhauling Wireless Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 9.3 Passive Optical Network (PON): Standards

and Technology Options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 9.4 Technology Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

9.4.1 TDM-PON. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 9.4.2 WDM-PON . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 9.4.3 OFDM-PON . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 9.4.4 Hybrid PONs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245

9.5 PON Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 9.5.1 GPON/EPON. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246

9.6 10G-PON . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 9.7 10G-Epon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

9.7.1 NG-PON2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 9.7.2 Evolution Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . 250

9.8 Challenges in PON Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 9.9 Distributed Ring-Based WDM-PON Architecture . . . . . . . . . . . . 251 9.10 Architecture Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 9.11 Allocation of Network Resources . . . . . . . . . . . . . . . . . . . . . . . . 255

9.11.1 Dynamic Bandwidth Allocation. . . . . . . . . . . . . . . . . . 256 9.11.2 Upstream Traffic Flows Rerouting and Sharing . . . . . . 256

9.12 Wavelength Assignment/Sharing for Downstream Traffic . . . . . . 257 9.13 Fault Detection and Recovery. . . . . . . . . . . . . . . . . . . . . . . . . . . 257

9.13.1 Fault Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 9.13.2 Fault Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259

9.14 Fronthauling Mobile Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 9.15 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262

Contents xvii

10 BBU Hotelling in Centralized Radio Access Networks . . . . . . . . . . . 265 Nicola Carapellese, M. Shamsabardeh, Massimo Tornatore and Achille Pattavina 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 10.2 Mobile Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 10.3 Evolving the Base Station: BBU and RRH. . . . . . . . . . . . . . . . . 267 10.4 Advantages of BBU Hotelling . . . . . . . . . . . . . . . . . . . . . . . . . . 268

10.4.1 Cost Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268 10.4.2 Energy Savings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 10.4.3 Improved Radio Performance . . . . . . . . . . . . . . . . . . . 269

10.5 Challenges of BBU Hotelling: Fronthaul . . . . . . . . . . . . . . . . . . 270 10.5.1 High, Constant Bitrate . . . . . . . . . . . . . . . . . . . . . . . . . 270 10.5.2 Maximum End-to-End Latency . . . . . . . . . . . . . . . . . . 271 10.5.3 Strict QoS Requirements . . . . . . . . . . . . . . . . . . . . . . . 273

10.6 RAN Architectures Based on BBU Hotelling . . . . . . . . . . . . . . . 273 10.6.1 Classification on BBU Placement . . . . . . . . . . . . . . . . 274 10.6.2 Classification on Fronthaul Transport . . . . . . . . . . . . . 276 10.6.3 Classification on BBU Implementation . . . . . . . . . . . . 278

10.7 An FMC Network Architecture for BBU Hotelling . . . . . . . . . . 280 10.7.1 General Network Architecture . . . . . . . . . . . . . . . . . . . 280 10.7.2 BBU Placement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 10.7.3 Traffic Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282

10.8 The BPTR Optimization Problem . . . . . . . . . . . . . . . . . . . . . . . . 283 10.9 A Heuristic Greedy Algorithm for BPTR . . . . . . . . . . . . . . . . . . 284

10.9.1 Notation and Input Data . . . . . . . . . . . . . . . . . . . . . . . 284 10.9.2 Heuristic Subroutines. . . . . . . . . . . . . . . . . . . . . . . . . . 285 10.9.3 Heuristic Scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286

10.10 A Case Study for the BPTR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286 10.11 Conclusion and Open Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290

11 Rethink Ring and Young: Green and Soft RAN for 5G. . . . . . . . . . 293 Chih-Lin I, Jinri Huang, Ran Duan, Gang Li and Chunfeng Cui 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 11.2 No More Cells: One Key 5G Vision . . . . . . . . . . . . . . . . . . . . . 294 11.3 Cloud RAN: The Key Enablers to NMC . . . . . . . . . . . . . . . . . . 296

11.3.1 The Concept of C-RAN. . . . . . . . . . . . . . . . . . . . . . . . 296 11.3.2 C-RAN Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298 11.3.3 Advantages of C-RAN . . . . . . . . . . . . . . . . . . . . . . . . 299

11.4 Challenges and Potential Solutions for C-RAN Realization . . . . 299 11.4.1 Challenges on Transport Networks for

Centralization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 11.4.2 Potential Fronthaul Solutions . . . . . . . . . . . . . . . . . . . . 300 11.4.3 Challenges on Virtualization Implementation

to Realize Resource Cloudification . . . . . . . . . . . . . . . 302

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11.5 Recent Progress on C-RAN from China Mobile . . . . . . . . . . . . . 304 11.5.1 Field Trials on Centralization with Different

FH Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 11.5.2 Exploitation of C-RAN Virtualization . . . . . . . . . . . . . 307

11.6 Evolving Toward 5G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310 11.6.1 C-RAN to Enable Key 5G Technologies. . . . . . . . . . . 310 11.6.2 Rethink CPRI: CPRI Redefinition . . . . . . . . . . . . . . . . 311 11.6.3 Edge Application on C-RAN. . . . . . . . . . . . . . . . . . . . 313

11.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315

Part IV Novel Management Strategies for Fi-Wi Networks

12 Next-Generation PoP with Functional Convergence Redistributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Philippe Bertin, Tahar Mamouni and Stéphane Gosselin 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 12.2 What Services at the Network Edge? . . . . . . . . . . . . . . . . . . . . . 321

12.2.1 Virtual Residential Gateway . . . . . . . . . . . . . . . . . . . . 322 12.2.2 Broadband Network Gateway . . . . . . . . . . . . . . . . . . . 324 12.2.3 Distributed Evolved Packet Core . . . . . . . . . . . . . . . . . 326 12.2.4 Highly Distributed Content Delivery Networks . . . . . . 328

12.3 The Path Toward Fixed and Mobile Convergence . . . . . . . . . . . 329 12.3.1 Converged Subscriber Data and Session

Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 12.3.2 Universal Access Gateway . . . . . . . . . . . . . . . . . . . . . 331

12.4 Implementing the NG PoP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 12.4.1 Design Principles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 12.4.2 Dimensioning the NG PoP . . . . . . . . . . . . . . . . . . . . . 334

12.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336

13 Coordinated Multi-point (CoMP) Systems. . . . . . . . . . . . . . . . . . . . . 337 Yizhuo Yang, Christina Lim and Ampalavanapillai Nirmalathas 13.1 Introduction on CoMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 13.2 Requirements on the Backhaul Network . . . . . . . . . . . . . . . . . . . 339

13.2.1 Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 13.2.2 Synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 13.2.3 Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340

13.3 Backhaul Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 13.3.1 GROW-Net Architecture . . . . . . . . . . . . . . . . . . . . . . . 342 13.3.2 FUTON Prototype . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 13.3.3 Adaptive Photonics-Aided CoMP for MMW

Small Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348 13.3.4 Converged Fiber–Wireless Architecture. . . . . . . . . . . . 349

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13.4 Fiber–Wireless Integration Schemes Enabling CoMP . . . . . . . . . 350 13.4.1 BS Configuration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 13.4.2 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 352 13.4.3 Implementation of CoMP . . . . . . . . . . . . . . . . . . . . . . 354 13.4.4 Experimental Demonstration . . . . . . . . . . . . . . . . . . . . 355

13.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357

14 Converged Wireless Access/Optical Metro Networks in Support of Cloud and Mobile Cloud Services Deploying SDN Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 Anna Tzanakaki, Markos Anastasopoulos, Bijan Rofoee, Shuping Peng, George Zervas, Reza Nejabati, Dimitra Simeonidou, Giada Landi, Giacomo Bernini, Roberto Monno, Nicola Ciulli, Gino Carrozzo, Kostas Katsalis, Thanasis Korakis, Leandros Tassiulas, Georgios Dimosthenous, Dora Christofi, Jordi Ferrer Riera, Eduard Escalona, Jacopo Pianigiani, Dirk Van Den Borne and Gert Grammel 14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360 14.2 Existing Technology Solutions Supporting Cloud

and Mobile Cloud Services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362 14.2.1 Physical Infrastructure Solutions

Supporting Cloud Services . . . . . . . . . . . . . . . . . . . . . 362 14.2.2 Infrastructure Management . . . . . . . . . . . . . . . . . . . . . 363 14.2.3 Service Provisioning . . . . . . . . . . . . . . . . . . . . . . . . . . 365

14.3 Proposed Converged Network Architecture . . . . . . . . . . . . . . . . 366 14.3.1 Vision and Architectural Approach . . . . . . . . . . . . . . . 366 14.3.2 Physical Infrastructure Layer . . . . . . . . . . . . . . . . . . . . 369 14.3.3 Infrastructure Management . . . . . . . . . . . . . . . . . . . . . 371 14.3.4 Virtual Infrastructure Control Layer. . . . . . . . . . . . . . . 373 14.3.5 Converged Service Orchestration. . . . . . . . . . . . . . . . . 378

14.4 Architecture Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 14.4.1 Network Scenario and Related Work. . . . . . . . . . . . . . 379

14.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387

Conclusion and Future Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395

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Introduction

Abstract Fifth-generation mobile access networks will feature unprecedented performance, not only in terms of higher data rates and lower latencies, but also in terms of the “intelligence” of the network. To achieve these targets, 5G networks will resort to solutions such as small-cell deployment (micro-, femto-, etc.), coor- dinated multi-cell processing (CoMP, eICIC) and centralized/cloud RAN. Such techniques will ultimately burden the optical fiber access/aggregation network needed to backhaul the mobile traffic, as this section of the network will be responsible to serve massive traffic with very strict latency. Hence, the trend toward an actual convergence of fiber and wireless technologies in access/aggregation networks, which has been emerging in the last decade, is expected to gain increased importance, as it indirectly impacts on radio access performance. This book introduces several key enabling techniques for next-generation fiber-wireless con- vergent communication systems in 5G and mobile backhaul networks. The book features fourteen excellent contributions, authored by well-renowned industrial practitioners and academic researchers, in three main thematic areas: system tech- nologies (radio-over-fiber, millimeter-wave transmission, SDN-controlled optical technologies, LTE-advanced), network architectures (TWDM PON, BBU Hotelling, 5G RAN), and network management (SDN-controlled access, CoMP, Next Generation Point of Presence). The focus of various chapters goes beyond the description of state of the art, also presenting the evolutionary paths for fiber- wireless convergence toward 5G implementation.

The overall picture

Mobile traffic growth due to the proliferation of smart mobile devices and band- width demanding applications is accelerating the evolution of radio access networks (RAN) from 2G, 3G, to 4G, and beyond. Looking into the future, 5G wireless technology is on the horizon. The exact definition of 5G is still under active

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discussions, and many research activities are being carried out in both industry and academia. For link spectrum efficiency, the existing wireless technologies have been pushed very close to the Shannon theoretical limit by using higher-level modulation formats (e.g., 64QAM-OFDM), multiple-input and multiple-output (MIMO) techniques, and advanced channel coding schemes (e.g., low-density parity-check (LDPC) and turbo codes). However, there is a great potential in enhancing the system spectrum efficiency. For example, by coordinating adjacent cells to jointly transmit signals to cell-edge users, coordinated multi-point trans- mission (CoMP) [2] can significantly improve the data rate of the users who used to suffer from strong inter-cell interference, thus enhancing the system spectrum efficiency (see Chap. 13). In another dimension, people are always looking for more spectrum, mostly in higher radio frequencies, e.g., exploring the millimeter-wave band [3, 4] (see Chap. 6). Last but not the least, small-cell solutions are being increasingly implemented and are becoming the trend for future wireless commu- nication. By reducing the cell size, limited spectral resources can be reused among cells more frequently, thus enhancing the total system capacity. There are also many discussions on merging WLAN (e.g., Wi-Fi) with cellular small cells to provide a uniform platform for 5G wireless access (see Chap. 2). The main directions of future wireless communications [1] will be overviewed in more detail in Chap. 3.

On the other hand, supporting such techniques will strain the optical fiber-based access/aggregation network that provides the backhaul for the wireless access network. Such “mobile backhaul” or “mobile transport” network will be required to serve the massive traffic amount coming from the mobile users (generated via a plethora of broadband mobile applications) with very strict latency constraints. Hence, the trend toward an actual convergence of fiber and wireless technologies in access/aggregation networks, which has been emerging in the last decade, is expected to have an increased importance, as it indirectly impacts on radio access performance.

In this book, aligned with the directions of future 5G networks, several key enabling technologies, network architectures, and management/control approaches for fiber–wireless convergence for 5G mobile transport networks are introduced.

More importantly, the concept of centralized radio access network (CRAN), as well the associated concept of RAN virtualization (cloud RAN), is comprehensively reviewed in the book. CRAN is a recent solution which radically changes the classical architecture of the RAN. It separates mobile baseband units (BBU) from corresponding Remote Radio Heads (RRH) and consolidates them into common locations, also known as “hotels” (in fact, the paradigm of CRAN is often referred to as BBU hoteling or hosteling). Each BBU/RRH pair exchanges digital radio-over-fiber (D-RoF) data (see Chap. 5), also known as “fronthaul,” obtained from the baseband digitization of radio interface signals (see Chap. 4). Thanks to the sharing of backplanes, power, computational, and maintenance resources of BBUs hosted in the same hotel, a significant amount of OPEX saving can be achieved by adopting this mobile backhauling architecture (see Chap. 10). Moreover, increased coordination will allow to optimize RAN throughput (e.g.,

xxii Introduction

using the previously mentioned CoMP technique, see Chap. 14), and to even move towards more exciting and future-prone CRAN paradigms as the “No More Cells” approach (see Chap. 11). Note that, conventionally, backhaul/fronthaul networks can be based on T1 lines, microwave point-to-point links, and not only fiber links. However, since the backhaul/fronthaul capacity requirement increases drastically as the wireless access techniques evolve, optical fibers are considered as the ultimate transport media to provide sufficient bandwidth as well as future-proof capacity upgrade. In particular, passive optical networks (PONs) are a relevant candidate for mobile backhaul/fronthaul applications (see Chaps. 8 and 9).

In light of the previous discussion, it clearly emerges that the envisioned fiber- wireless-convergent backhaul network will require enhanced “intelligence” in terms of reconfigurability and coordination, among others. To enable such intelligence, research is currently focusing also on the role that software-defined networking (SDN) will play as an enabler for advanced functionalities for fiber-wireless con- vergence (see Chap. 7, for applications of SDN in the next-generation optical access segment, and “Conclusion and Future Topics”, for a proposal of an SDN-controlled fiber-wireless converged network supporting cloud computing services).

Finally, there is increased interest from network operators in leveraging access/aggregation infrastructure for fixed traffic to also perform backhaul/fronthaul of mobile traffic, in a related concept to fiber-wireless convergence, which is typ- ically referred as fixed-mobile convergence (see Chap. 13) [5].

The topic of fiber-wireless convergence is currently an actively researched topic, and clearly it is impossible to cover in this book all the latest progress in the field. Nevertheless, as of today, a comparably comprehensive collection of up-to-date, relevant, and logically organized works on this topic has not been yet made available. The aim of this book in putting together this collection of chapters is to ensure that experienced as well as novice researchers can have a single handy source of reference on this topic. The following chapters do not only cover exciting long-term research proposals originating mostly from academia and research lab- oratories but they also contain the current vision of practitioners working in leading technology vendor and network operator companies.

The intended audience of the book consists of students in academia learning about and doing research on fiber-wireless convergent networks in support of 5G and mobile backhaul networks, industrial practitioners that are evaluating the introduction of these technologies in the design of current 4G network as well as those starting to build a 5G-prone backhaul infrastructure, and faculty members and researchers in academia wishing to teach an advanced course on next-generation network design, or conducting research in the area of fiber–wireless convergence and 5G. The objective of the book is to provide the reader with a comprehensive source of information on this interesting and timely topic that can foster novel ideas and research lines, as well as inspire new ways to go forward and find new solutions for the numerous remaining challenges.

Introduction xxiii

Book Organization

The book is organized into four parts covering different subject areas. Part I (Chaps. 1–3) overviews market and technological trends that motivate the book and dis- cusses the benefits of convergence. An introduction to the evolution of cellular technology toward 5G is also provided for readers that need to catch up with recent trends, especially in LTE. Part II (Chaps. 4–7) focuses on enabling technologies for fiber-wireless convergence. Part III (Chaps. 8–11) deals with rising paradigms for network architectures in the fiber-wireless convergent access/metro network seg- ments, and Part IV (Chaps. 12–14) discusses some challenges in terms of man- agement of a fiber-wireless converged network. In the last chapter of the book, we conclude the book and overview very novel and recent areas of research which have been emerging during the preparation of the book.

Chapter 1 by Ma and Jia provides an introduction to technical trends and market status of both broadband wireline and wireless access networks, and also summa- rizes the current forecasts on technology evolution of fiber-wireless networks. As a useful introduction to technologies discussed throughout the rest of the book, the chapter overviews broadband wireline access networks including xDSL, coaxial cable, and hybrid fiber coax (HFC), and various PON architectures and then moves to broadband wireless access technologies such as Wi-Fi, WiMAX, and mobile cellular systems.

Chapter 2 by Sivarajan and Mohapatra presents in a comprehensive manner the foundational concepts of the design principles used in LTE-A radio access net- works, such as bandwidth aggregation, transmission diversity, interference man- agement, and MIMO spatial diversity. These concepts are instrumental to the entire book as they provide a clear technical roadmap to the 5G technologies.

Chapter 3 by Chang and Cheng introduces the concept of multi-tier radio access network (RAN) combining the strength of fiber-optic and radio access technologies. This concept, which will be complemented by the specific contributions in the rest of the book, employs adaptive microwave photonics interfaces and radio-over-fiber (RoF) techniques for future heterogeneous wireless communications. Coexistence of current and future mobile network standards such as 4G and 5G with optimized and continuous cell coverage using multi-tier RoF, regardless of the underlying network topology or protocol, is also discussed.

Chapter 4 by Gagnaire overviews the basic principles, drawbacks, and benefits of the various analog- and digital radio-over-fiber techniques currently available, by providing a detailed state-of-the-art picture of the level of advancement of these two techniques. These techniques are a key for the implementation of the signal transport in the CRAN architectures. A detailed overview of the current RoF options provides a useful insight on the transmission technologies that support the communication between RRH and BBU in CRAN.

Chapter 5 by Frigerio, Lometti, and Sestito introduces the reader to the current standardization activities related to D-RoF, namely those relative to the definition of the digital interface between radio units and base stations (CPRI, OBSAI, ORI)

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Anis Shallouf
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and those aimed at allowing for the transport of such interfaces over a geographical network. The latest aspect is specifically important in view of the modern CRAN paradigm.

Chapter 6 by Yu summarizes several different approaches for the realization of large capacity (>100Gb/s) fiber-wireless integrated systems, including optical polarization-division-multiplexing (PDM) combined with MIMO reception, advanced multi-level and multi-carrier modulation, antenna polarization multi- plexing, and multi-band multiplexing. These spectral efficient modulation and multiplexing techniques are important for providing high-speed, high-capacity, free space transmission links as an alternative to fiber based mobile backhaul and fronthaul systems to overcome difficult terrains or fiber-cut.

Chapter 7 by Cvijetic and Wang introduces the reader to system-level challenges for SDN in fiber-wireless networks, including important aspects from both the control and data plane perspectives. The requirements for and ramifications of SDN-based control in fiber-wireless networks are examined, and a survey of recent research and development advances in SDN for fiber-wireless (at both the system and network levels) is also presented.

Chapter 8 by Liu reviews several existing and emerging fiber-wireless access networks including macrocells, small cells, distributed antenna systems, and cloud RAN. A novel cloud radio-over-fiber access network is subsequently introduced as a promising fiber-wireless convergent access architecture for future 5G networks. Proof-of-concept experiments are presented to demonstrate multi-operator/ multi-service infrastructure-sharing capabilities in the cloud-RoF systems.

Chapter 9 by Ellinas et al., reviews passive optical network (PON) architectures as it is generally accepted that fiber deployment to cell towers is the only future-proof solution to build mobile backhauls, which will scale to the increased capacity requirements of future NG-WBAN technologies. Further, among the optical network architectures, PONs meet the needs for such a high-capacity access architecture. Different PON technologies, including TDM-PON, WDM-PON, OFDM-PON, and hybrid TDM-WDM, OFDM-WDM co-designs are described, followed by PON technology standards (GPON/EPON, 10G-PON, 10G-EPON, NG-PON2) and evolutionary scenarios. A novel fully distributed ring-based WDM-PON architecture is subsequently introduced as a promising access archi- tecture that not only enables the support of a converged 4G/5G mobile infrastructure but also supports distributed network control as well as management operations.

Chapter 10 by Carapellese, Shamsabardeh, Tornatore, and Pattavina focuses on the role of BBU hoteling in fiber-wireless converged architectures. A classification of the various architectural solutions for BBU hoteling is given, regarding BBU placement and implementation, and fronthaul transport. The authors introduce a novel network optimization problem, namely the BPTR (BBU Placement and Traffic Routing) problem, which addresses the questions of “how and where to place the BBUs” so as to minimize the number of hotels or the transport capacity of the network.

Chapter 11 by I, Huang, Duan, Li, and Cui follows a different, evolutionary approach for 5G systems termed “No More Cells” (NMC). NMC transfers the

Introduction xxv

traditional cell-centric network design to a user-centric design principle. It is pointed out that NMC realization could be facilitated by the CRAN architecture, which is demonstrated and verified through extensive field trials.

Chapter 12 by Bertin, Mamouni, and Gosselin discusses how to realize con- vergence in the framework of the Next Generation Point of Presence (NG PoP). The NG PoP is a new concept of a flexible platform in the telecom network hierarchy that combines aggregation of fixed and mobile access traffic, IP edge routing, and the ability of hosting additional network functions and services. In addition, two more essential concepts in the path towards convergence are intro- duced, namely the converged subscriber data and session management and the universal access gateway.

Chapter 13 by Yang, Lim, and Nirmalathas provides a brief introduction on the cooperative multiple point (CoMP) technologies and the backhaul requirements for enabling CoMP techniques in LTE-A. The CoMP backhaul architectures and CRAN configurations based on various RoF technologies are also presented.

Chapter 14 authored by several of the researchers working on the EU project CONTENT proposes a next-generation converged infrastructure to support fixed and mobile cloud computing services. The proposed infrastructure facilitates effi- cient and seamless interconnection of fixed and mobile end users to computational resources through a fiber-wireless convergent network. The proposed architecture is well aligned and fully compliant with current SDN paradigms.

Finally, the conclusion elaborates on the diverse contributions of the book and how these contributions are brought together toward the ultimate goal, namely the implementation of the 5G mobile access network. Further, it overviews a set of rising and recent areas of research which have been emerging during the preparation of the book, such as the impact of Internet of Things and Mobile Edge Computing on fiber-wireless convergence, as well as the new proposals of “mid-haul” solutions (also called “split function” solutions) to decrease the fronthaul traffic on CRAN. Since energy efficiency becomes a critical challenge of 5G systems, a short dis- cussion is also included on potential solutions to this issue, including recourse a location, network planning, renewable energy, and hardware architectures.

Massimo Tornatore Gee-Kung Chang

George Ellinas

References

1. Alamouti S (2012) Networks of the future—Some challenges ahead, wireless communication and networking conference (WCNC), keynote speech

2. Lee D, Seo H, Clerckx B, Hardouin E, Mazzarese D, Nagata S, Sayana K (2012) Coordinated multipoint transmission and reception in LTE-advanced: deployment scenarios and operational challenges. IEEE Commun Mag 50(2):148–155

xxvi Introduction

3. Pi Z, Khan F (2011) An introduction to millimeter-wave mobile broadband systems. IEEE Commun Mag 49(6):101–107

4. Yu J, Chang G-K, Jia Z, Chowdhury A, Huang M-F, Chien H-C, Hsueh Y-T, Jian W, Liu C, Dong Z (2010) Cost-effective optical millimeter technologies and field demonstrations for very high throughput wireless-over-fiber access systems. IEEE/OSA J Lightwave Technol 28 (16):2376–2397

5. Gosselin S, Pizzinat A, Grall X, Breuer D, Bogenfeld E, Krauß S, Torrijos Gijón J, Hamidian J, Fonseca N, Skubic B (2015) Fixed and mobile convergence: which role for optical networks?. IEEE/OSA J Lightwave Technol 7(11):1075–1083

Introduction xxvii

Part I The Path Towards Convergence

Chapter 1 Future Radio Access, Wi-Fi-LTE, LTE-Advanced: The Path to 5G

Rajarajan Sivaraj and Prasant Mohapatra

Abstract With the proliferation of IP-based bandwidth-intensive video services and smartphones, there has been an unprecedented exponential increase in mobile broadband data. This has resulted in increasing demand for additional wireless capacity. In order to increase the wireless capacity multifold, the next-generation radio access networks (RAN) boast of a number of sophisticated technologies, such as Carrier Aggregation (CA), Evolved-Multicast/Broadcast Multimedia Services (eMBMS) using Single-Frequency Networks (SFN), enhanced Inter-Cell Interference Coordination (eICIC) in self-organized Heterogeneous Networks (HetNets), Coordinated Multi-Point (CoMP) transmission in Multiple-Input– Multiple-Output (MIMO) systems using 2D/3D Beamforming, and full-duplex communication. Some of the above technologies are standardized in 3GPP Release 10+ systems like LTE-Advanced and are seen as a roadmap to 5G RANs. This chapter provides a comprehensive overview of each of these technologies and sur- veys the key open issues concerning them in terms of radio resource management (RRM) to facilitate maximum wireless capacity and provide Quality-of-Service (QoS) to the users. It also explores the synergies between these technologies towards developing holistic optimization techniques for the design of 4G+ and 5G systems.

1.1 Introduction

Recent proliferation of mobile broadband data is accelerated by the unprecedented increase in the subscription of next-generation bandwidth-intensive multimedia ser- vices by IP-based smartphone and tablet/computer users. 3GPP LTE, the latest 4G wireless broadband standard based on OFDMA, promises higher data rates than its predecessors from the legacy 3GPP systems. This is due to the independently mod- ulated orthogonal and flat-fading sub-carriers that constitute a frequency-selective OFDM carrier. Furthermore, the multi-user diversity feature of OFDMA enables

R. Sivaraj (&) � P. Mohapatra Department of Computer Science, University of California, Davis, CA, USA e-mail: rsivaraj@ucdavis.edu

© Springer International Publishing Switzerland 2017 M. Tornatore et al. (eds.), Fiber-Wireless Convergence in Next-Generation Communication Networks, Optical Networks, DOI 10.1007/978-3-319-42822-2_1

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multiplexing different users with different requirements, by supporting different Modulation and Coding Scheme (MCS) rates on every sub-channel. However, the LTE operators face a significant challenge in satisfying the Quality-of-Service (QoS) demands of the multimedia services, such as HD video streaming/gaming and video broadcast, due to the limited and expensive resources of the licensed spectrum. This challenge is further compounded by the channel dynamics of the network due to factors such as user mobility, inter-cell interference, fading, and attenuation. Especially, the users close to the cell edges are significantly penalized. This is because of their poorer channel quality that they are not being allocated adequate radio resources to satisfy their QoS [1].

An effective Radio Resource Management (RRM), accounting for the dynamic channel characteristics and traffic demands of the user, is essential in meeting the QoS objectives of the services offered by the network operator, and in turn en- hancing the Quality-of-Experience (QoE) of the subscriber. Hence, an efficient RRM strategy calls for continuous innovation in the state of art for network con- figuration, effective deployment, and utilization of the spectrum resources to improve the system performance. This chapter provides a comprehensive overview on the latest advancements in RRM techniques that serve as a roadmap to 5G telecommunication deployments. It also delineates the challenges and open issues associated with each of these techniques. The 3GPP Release 10+ standardizes some of the techniques detailed in this chapter.

LTE-A aims to meet the advanced requirements of International Mobile Telecommunications (IMT) to support high downlink data rates of up to 1 Gbps for low-speed mobile or stationary User Equipments (UE) or 100 Mbps for high-speed mobile UEs and peak uplink rates of up to 500 Mbps, for facilitating the next-generation telecommunication services. IMT requirements for LTE-A gener- ally aimed at improving the average performance and spectral efficiency of cell-edge UEs, rather than enhancing the peak spectral efficiency of individual applications (such as VoIP) [1].

1.1.1 LTE Principles of Operation and Deployment

Each LTE mobile network operator is auctioned chunks of licensed frequency sub-bands. A licensed frequency sub-band deployed over each LTE base station, called Evolved Node B or alternatively, eNB, with scalable bandwidths ranging from 1.4 to 20 MHz (that includes 1.4, 3, 5, 10, 15, and 20 MHz), is called a Component Carrier (CC). Each CC consists of orthogonal, independently modulated and flat-fading sub-carriers (where a single distinct modulation scheme is used within the frequency domain of one sub-carrier), such that each sub-carrier has a phase shift of 90° with the adjacent ones [2, 3]. Each sub-carrier in LTE is 15 kHz in bandwidth. There are around 1200 sub-carriers in a 20-MHz CC. Each LTE frame consists of 10 sub-frames of 1 ms duration each. The minimum unit of radio resource allocation in the two-dimensional time–frequency domain is a Physical Resource Block (PRB) [4]. It consists of 12 OFDMA sub-carriers, pointing to a frequency sub-band

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chunk, from the frequency domain and 7 symbols or a half sub-frame (0.5 ms) from the time domain. The bandwidth of a PRB is 180 kHz. Considering the compromise in bandwidth due to the guard band interval between sub-carriers needed to preserve orthogonality, mitigate inter-symbol interference, and maintain significant cyclic prefixes, the number of PRBs in a 20-MHz CC is 100. Any User Equipment (UE), admitted for service by an LTE eNB, is allocated at least two PRBs. Each UE sends channel quality feedback to the eNB, which uses this information to choose an appropriate Modulation and Coding Scheme (MCS) rate, supported by the UE, for encoding the data signals to be transmitted to the UE. The encoded data rate is a function of the MCS, where a higher MCS rate indicates a higher amount of data delivered to the UE. The recent advancements in radio access technologies are broadly based on the following principles:

a. Bandwidth aggregation: This technique is employed to increase the band- width of the radio access network by aggregating spectrum resources, deployed in the form of Component Carriers (CC). It is one of the design techniques supported in LTE-Advanced systems from 3GPP Release 10 onwards, standardized by the term Carrier Aggregation (CA) [4, 5]. More than one CC, belonging to the same or different central band frequencies, are integrated as an aggregated carrier and deployed over the LTE eNBs. At most 5 different CCs can be aggregated and deployed over an LTE eNB, resulting in a maximum possible bandwidth of 100 MHz. Hence, a LTE-A eNB supporting CA can serve more than one cell, as shown in Fig. 1.1. Theoretically, CA facilitates peak downlink rates of up to 1 Gbps for stationary/low-speed mobile UEs and around 100 Mbps for high-mobile UEs [1].

Fig. 1.1 Carrier aggregation serving more than one cell per eNB

1 Future Radio Access, Wi-Fi-LTE, LTE-Advanced: The Path to 5G 5

b. Transmission diversity: In this technique, the same information signals are transmitted from more than one eNB that are synchronized to jointly schedule the UEs, as shown in Fig. 1.2. The CCs deployed across the synchronized eNBs belong to the same central band frequency, forming a Single-Frequency Network (SFN) [6–9]. The eNBs coordinate with each other to schedule a common set of UEs on the same PRBs across the eNBs using the same MCS rates, based on the radio channel characteristics from each synchronized eNB. This results in con- structive interference at the UE and causes diversity gains, due to a decrease in the number of interfering eNBs and (consequently) an increase in the number of signal-transmitting sources. This principle is used in Evolved-Multicast Broadcast Multimedia Services (eMBMS) feature of LTE [6], standardized in 3GPP Release 8, and in Coordinated Multi-Point transmission feature (CoMP), supported by LTE-Advanced [1]. While the former leverages the diversity gains to improve multicast performance, the latter enhances the performance of cell-edge UEs that otherwise have a higher cell-outage probability.

c. Network Heterogeneity: This design principle aims at enhancing the per- formance of LTE macrocell networks through small-cell deployments, as shown in Fig. 1.3. Small-cell eNBs are cheaper, lower power eNBs that are densely deployed within the coverage region of one or more LTE macro-eNBs [10, 11]. A dedicated set of radio resources are deployed in the form of CCs over the small-cell eNBs. This increases the network capacity, especially when the macro-cell eNBs face a shortage of residual resources in case of high data demand. Small-cell deployments also provide extended network coverage to macro-cell UEs, especially to the ones at the cell edges of the macro. LTE-Advanced small cells typically use CCs belonging to the same central band frequencies as the ones in the macrocell eNBs, resulting in higher frequency reuse. However, the deployment can lead to high co-channel interference, if resource sharing is not carefully planned. The UEs in the network have a larger degree of freedom in their cell association, as each of them could associate with either one of the small cells or an interfering macro.

d. Spatial diversity and multiplexing: Spatial diversity employs at the eNB multiple transmit antennae with identical design that are physically separated from each other, usually by at least a half-wavelength. In conventional single-stream

Fig. 1.2 Synchronized eNBs serving UEs

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beamforming, the same signal is transmitted from each transmit antenna in the array with appropriate weights, based on phase and channel gain, so as to maximize the throughput. Antenna arrays mitigate the destructive multi-path interference among the reflections of the transmitted signal by leveraging the different transmission and fading characteristics of each antenna with respect to the receiver, and combining them at the receiver. This improves the downlink channel quality at the UE. Spatial diversity is one of the operating principles used in Multiple-Input–Multiple-Output (MIMO) systems, shown in Fig. 1.4, where the downlink capacity is further enhanced by using multiple receiver antennae at the UE. In order to maximize the throughput of the UE equipped with multiple receiver antennae, a multi-stream transmission using spatial multiplexing is considered [12]. For a given sub-channel, a channel matrix is constructed for each eNB–UE pair from the channel quality value between each transmit–receive antenna pair. Multi-stream beamforming uses

Fig. 1.3 Coexistence of heterogeneous transmission devices (macrocell, small cells)

Fig. 1.4 Communication over a multi-antenna MIMO channel

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this matrix to independently encode multiple data streams using a precoding vector and transmitting the information signals from each of the antennae in the array. This spatial multiplexing of different downlink data streams onto the MIMO channel exploits the additional degree-of-freedom gain, considering the varying channel characteristics between each transmit–receive antennae pair at the eNB and UE, respectively. Multiuser MIMO-OFDMA with 2D/3D-beamforming features is a recent sophistication, actively considered and tested in LTE-Advanced and 5G deployments.

The above principles are on the basis of LTE and will partake in shaping the 5G technology and, in turn, influence the backhaul infrastructure from the perspective of Fi-Wi convergence. This chapter discusses some of the open issues in each of the aforementioned radio access techniques from the perspectives of PHY and MAC layers. In the rest of this introductory section, we quickly introduce the main open issues at PHY and MAC layer that will be elaborated in the rest of the chapter.

PHY Layer: The challenges in the PHY layer are typically concerned with improving the channel quality of the users, e.g., increasing the Signal-to-Interference-plus-Noise Ratio (SINR) of the individual UEs. The factors that affect SINR include assignment of CCs to the UEs as the central band fre- quency of the CC impacts the path loss yielded to the UE, appropriate cell asso- ciation based on its link quality with the UE, appropriate choice of precoding/beamforming vectors to manage the signal strength and inter-cell inter- ference for the UEs multiplexed onto the MIMO channel, the number of eNBs synchronized in an SFN which impacts the diversity gain of the UEs served by the SFN, etc. The channel quality of the UEs helps in determining appropriate MCS rates chosen by the eNBs in serving the UEs and subsequently, the net system capacity. The crucial component in the net system performance is the channel quality of the cell-edge UEs. (e.g., in applications like eMBMS, the performance of a multicast/broadcast group is limited by the UEs, especially at the cell edges, who have minimum throughput).

MAC Layer: The issues in the MAC layer are centered on the allocation of frequency–time PRBs and scheduling them to the UEs. The key utility metrics in the MAC layer are throughput, fairness, and effective spectrum utilization. Throughput deals with achieving a higher net system capacity as a result of allo- cating PRBs and choosing appropriate MCS rates to serve the UEs, whereas fair- ness deals with allocating a fair share of PRBs to every UE in the network, accounting for its radio channel characteristics and traffic dynamics. Scheduling schemes like Proportional Fairness (PF) [13] balance the trade-off between throughput and fairness, where the related system utility function is seen as a logarithmic function of the throughput rates. Scheduling frequency–time PRBs to the UEs in the network impacts the Quality-of-Service (QoS) of the applications served by the eNBs and helps in evaluating related performance metrics, such as call/session admissibility.

Effective spectrum utilization is a measure of the fraction of the net deployed frequency–time PRBs that is scheduled to the UEs with appropriate MCS values. A higher value of this metric indicates that the licensed spectrum resources (in the

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form of PRBs) are being effectively deployed by allocating them appropriately to the UEs. Proportional Fair (PF) schedulers are also used to enhance the effective spectrum utilization of the eNB, as the sum of the logarithmic rates of the UEs would be higher if the eNB schedules a larger number of UEs with the best-possible MCS rates. The linear additive factor in the sum log rate helps in serving higher number of UEs and the log factor in the metric helps in ensuring fairness to each of them.

1.2 Carrier Aggregation

1.2.1 Definitions and Terminologies

3GPP LTE-Advanced (LTE-A) attempts to serve the next-generation telecommu- nication services, such as real-time high-definition video streaming, mobile HDTV, and high-quality video conferencing. LTE-A facilitates higher data rates in response to the requirements proposed by IMT-Advanced for providing higher QoS to mobile applications. LTE-A provides peak uplink and downlink data rates of 500 Mbps and 1 Gbps, respectively, for low-speed UEs and around 100 Mbps for fast-moving users [14, 15]. The bandwidths of LTE-A systems in both uplink and downlink can go up to 100 MHz, achieved by the aggregation of individual CCs through Carrier Aggregation (CA). Each CC corresponds to a cell or a coverage region and serves the UEs present in the region, which are associated with the cell. So, an LTE eNB supporting CA can serve more than one cell. An LTE-A Release 10+ UE can be scheduled on more than one CC, unlike an LTE Release 8/9 UE that can be scheduled on at most one CC. However, LTE-A UEs, supporting CA, are backward-compatible with LTE UEs, supporting operation on only one CC at the eNBs.

When an LTE-A UE attaches to an eNB and establishes or re-establishes a radio resource control connection with the eNB, only one cell corresponding to a CC is configured for the UE. This is termed as the primary cell. The CC corresponding to the primary cell is termed as primary CC. Then, depending on the serving traffic load on the primary CC and the Quality-of-Service (QoS) requirements of the UE, additional serving cells can be configured on the UE, termed as secondary cells [14]. The CCs corresponding to the secondary cells are called secondary CCs. Hence, an LTE-A UE can be associated with more than one cell. For every LTE-A UE, the primary cell is configured mandatorily, and the other configured secondary cells are based on the QoS requirements of the traffic profiles subscribed by the UE. The primary CCs across the LTE-A UEs need not be the same as in the aggregated carrier; they are UE-specific and can be different for different UEs served by the eNB. While both the primary and secondary CCs are involved in scheduling their PRBs to the UEs, the additional responsibility of the primary CC is to maintain the Radio Resource Control (RRC) connection of the corresponding UEs, which

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includes state information about location registration, connection establishment/re-establishment, termination, and handover. An LTE-A UE has only one RRC connection with the network. It establishes/re-establishes the RRC con- nection (using the random access procedure which registers the UE in the network) on the primary cell and uses additional RRC signaling to add, remove, or re-configure secondary cells. Hence, the primary CC for every UE cannot be changed dynamically as long as the UE is associated with an eNB, unlike the secondary CCs which can be dynamically configured and managed [5, 14, 15].

The PHY layer channel quality measurements taken by each UE are used to assist configuring, assigning, and managing CCs for the UE. The UE sends these channel quality values to the eNB using the uplink control channel on its primary CC. Although multiple cells are configured or assigned for an LTE-A UE, the UE is assigned a single-cell radio network temporary identifier, corresponding to the cell ID of the primary cell. This is used to uniquely identify the RRC connection of the UE for transmitting scheduling information on the downlink control channel cor- responding to either the primary CC or the secondary CCs. The primary CC for a UE is selected through either a channel-aware or a traffic load-balancing technique. Typically, the former assigns the CC which yields the highest Reference Signal Received Power (RSRP, a measure of the received signal strength, discussed in Sect. 1.4.1) or conversely the lowest path loss to the UE, as the primary cell. Load-balancing can be used to designate the primary and secondary CCs. The CC with the highest number of residual PRBs, after serving the current traffic load, is selected as the primary CC for the UE. This is done to make sure that adequate PRBs are available to be allocated to the UE to satisfy its traffic demands. Additionally, this also helps in preventing resource exhaustion.

1.2.2 Types of Carrier Aggregation

The types of CA [16, 17] are as follows a. Intra-band contiguous CA manages the aggregation of CCs in adjacent

frequencies of contiguous bandwidths from the same frequency band [5]. It is the easiest to implement as the CCs are adjacent to each other. The aggregated carrier is considered as a single enlarged wideband channel from the RF standpoint and hence, the UE requires only one transceiver. Even with the aggregated increase in bandwidth, the power consumption and cost requirements are considerably less stringent allowing for greater flexibility in RF design due to the already-existent multi-carrier nature of OFDM-enabled eNBs. However, it is difficult for the operator to obtain a contiguous large chunk of aggregated bandwidth, say up to 100 MHz, during the auctioning process due to competitive bidders, limited availability, and expensive deployment of licensed spectrum resources.

b. Intra-band non-contiguous CA manages the aggregation of CCs from non-adjacent, non-contiguous sub-band frequencies belonging to the same fre- quency band [5]. This design is more complicated than intra-band contiguous CA,

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as the multi-carrier signals from non-adjacent sub-bands can no longer be treated as a single distinct signal, thus requiring two or more transceivers. This adds com- plexity to the RF design in terms of power consumption and cost. However, this addresses the challenge in allocating large chunks of aggregated bandwidth due to the non-contiguity nature in the auctioning and deployment of non-adjacent CCs to every bidding mobile operator.

Moreover, the radio channel characteristics yielded by the CCs from the intra-band non-contiguous CA to any UE are not drastically different from each other, except for the random slow and fast-fading attenuations and Doppler shifts, as the CCs correspond to the same frequency band. An illustration of intra-band contiguous and non-contiguous CA is shown in Fig. 1.5.

c. Inter-band non-contiguous CA manages the aggregation of non-adjacent CCs belonging to different frequency bands [5]. The fragmented frequency sub-band chunks that are aggregated as CCs at the eNB are of varying bandwidths. As network operators may not win adjacent spectrum slots, they also aggregate bandwidths that may be not contiguous. Moreover, since each CC corresponds to a different central band frequency, the transmission characteristics such as path loss,

Fig. 1.5 Intra-band carrier aggregation

1 Future Radio Access, Wi-Fi-LTE, LTE-Advanced: The Path to 5G 11

Doppler shifts, fading and attenuation, pertaining to each CC, are different from one another. This results in different received signal values from each CC, measured by the corresponding RSRP values, as shown in Sect. 1.4.1. Figure 1.6 shows aggregation of CCs from different frequency bands. Hence, the different combi- nations of one or more CCs yield varying multiplexing gains for any UE, while being assigned to it. The UE requires the use of multiple transceivers to transmit/receive signals to/from the different aggregated CCs, thereby introducing additional complexity to the RF design in power and cost, as in the case of intra-band non-contiguous CA.

Carrier Aggregation is possible in both uplink and downlink directions. While there are a good number of similarities between uplink [4, 18] and downlink CA, in the following we highlight the significant differences between these two forms of CA.

Uplink CA: The performance of uplink CA is limited by the transmission power at the UEs. This limits the UEs from having high data rates yielded by the large bandwidths facilitated by CA. When LTE-A UEs transmit bandwidth-intensive multiple applications like HD videos in the uplink, they require being allocated larger PRBs and using higher modulation rates, in order to satisfy QoS and to make maximum usage of the allocated bandwidths, respectively. Both of these require- ments increase the Peak-to-Average Power Ratio (PAPR) [19] at the UEs and hence, result in a higher consumption of battery life at the UEs. The expected transmission power (in dB) for UE u as a result of transmitting to eNB m on CC c, given by P′u,m,c, is given in [4].

In order to reduce uplink power consumption, LTE uses Single-Carrier FDMA (SC-FDMA) [20] in the uplink, where the entire CC is available for the UE as a single channel, but the symbol time is of a much shorter duration, when compared to OFDMA. SC-FDMA requires allocation of contiguous PRBs across the deployed CC to each UE and this reduces the PAPR of the UE in the uplink. Moreover, the

Fig. 1.6 Inter-band carrier aggregation

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peak modulation rates availed by using SC-FDMA in the uplink are comparatively smaller than the ones obtained by using OFDMA in the downlink. Usage of higher MCS rates requires lower PRBs to be allocated to any UE for satisfying its QoS. But using higher rates increases the PAPR. On the other hand, using lower MCS rates requires using larger PRBs to be allocated to the UE. But, even larger PRBs increase the PAPR, as shown in [4]. However, a linear increase in PRBs only results in a logarithmic increase in the UE’s transmission power. So, SC-FDMA manages this trade-off by using lower peak modulation rates and contiguous chunk of PRBs, for which a collective feedback is sent less frequently.

Downlink CA: The eNBs are not so limited by power in their downlink as the UEs, in the uplink. So, LTE uses OFDMA in the downlink that supports higher peak MCS rates, thereby resulting in an increased PAPR. In the downlink, OFDMA also supports allocation of non-contiguous independently modulated PRBs to the UEs so as to maximize the gains resulting from frequency diversity. While most of the issues pertaining to RRM and cell-edge user performance [4] are applicable to downlink CA, the exclusive aspects of downlink CA deal with Evolved-Multimedia Broadcast/Multicast Services (eMBMS) [21, 8] and MIMO features in LTE-A. For video multicast/broadcast, the eNB serves groups of UEs, who collectively sub- scribe to the same multimedia content called session, on a common set of PRBs using common MCS rates [6].

The performance of a multicast/broadcast session is limited by the UE with the poorest channel conditions (especially those around the cell edges), as the throughput of a session is defined as the minimum throughput achieved by any UE, who subscribes to the session. This UE with the poorest channel conditions is designated at the bottleneck UE for the session. So, the MCS rate allocated by an eNB to serve a session would be the lowest rate supported by any UE in the corresponding eMBMS group.

Especially, when both cell-center and cell-edge UEs are a part of an eMBMS group, the poorer channel conditions of the cell-edge UEs and the lower MCS rates supported by them would drastically bring down the performance of the cell-center UEs in the group that exist with much better channel conditions [22]. Now, when UEs with drastically different channel conditions are grouped together, the eNB needs to jointly account for the channel dynamics of each individual UE along with the QoS requirements of the entire session in allocating PRBs. Due to the above requirements, in video multicasting services over LTE-A systems supporting CA, a common set of one or more CCs should be assigned to serve each group using the PRBs that constitute the CC(s) [23, 24]. There should be one common primary CC to carry out RRC-related functionalities for the entire group, which is a major challenge, as each UE in the network may choose a different primary CC from the rest due to varying channel conditions, traffic and mobility patterns. Similarly, the other optional secondary CCs, if configured based on the QoS requirements for the session, must also be common for the entire group. However, the key difference here is that the choice of selection of secondary CCs, allocation of PRBs, and assignment of MCS values to the eMBMS groups for QoS need not be limited by

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the UE with the poorest channel conditions in each group. Let us understand this better via the following illustration (Fig. 1.7):

Illustration: We provide a simple illustration to discuss video multicast for downlink CA [25, 26] that downstreams a given source video into several inde- pendently encoded bit streams called layers with different resolution. The layer with the minimum resolution that can be supported by even lower underlying network bandwidths is called base layer, and the other layers are called enhancement layers. Now, to facilitate scalable video multicast over LTE-Advanced systems supporting CA, the mandatory base layer, which must be decoded by every UE in the eMBMS group, is scheduled over the primary CC, designated for the group. This selection of primary CC is, in turn, based on the channel dynamics of each UE present in the eMBMS group. As mentioned earlier, the primary CC is chosen based on either a channel-aware or a traffic load-balancing technique. Accordingly, the allocation of the primary CC should be such that it yields the required channel conditions and residual PRBs, adequate enough to provide the highest-possible data rates to the bottleneck UE for the session and/or satisfy its QoS requirements. In inter-band aggregated carrier, the primary CC for the group is usually considered as that CC corresponding to a lower central band frequency and that has a higher number of residual PRBs. A CC with a lower central band frequency yields a lower path loss value to the UEs of the group, especially crucial to those present around the cell edges. Hence, this results in the primary CC providing sufficient channel conditions required to decode the mandatory base layer of the HD video by all UEs (incl. the cell edge UEs) of the group that subscribe to the video. If the bandwidth offered by the primary CC is sufficient to serve the mandatory base layer specified by the Guaranteed Bit Rate (GBR), which is ensured by the operator to the session sub- scribers, then the optional enhancement layers are served to the group on the secondary CCs configured for the group [3, 22]. Now, the enhancement layers are not mandatory to be served; however, a higher number of enhancement layers served with best effort for the group subscription increases the overall session throughput, having as an upper bound the maximum bit rate (MBR) [3, 22]. So, the optional secondary CCs can schedule the enhancement layers, and the choice of

Fig. 1.7 Scalable video coding

14 R. Sivaraj and P. Mohapatra

PRBs and MCS rates on the secondary CCs is not limited by the bottleneck UE of the eMBMS group subscribing to the session.

1.2.3 Radio Resource Management Framework for CA

The functionalities of the RRM framework for an LTE-A system supporting CA [27] are shown in Fig. 1.8. The eNB performs session admission control based on the QoS requirements and service class priorities of different UEs. A new RRM functionality, added to LTE-Advanced, is Layer-III CC Assignment and Configuration which configures and assigns a set of CCs for each UE. The other RRM functionality is Layer-II Packet Scheduling, which deals with the allocation of PRBs to the different UEs that are multiplexed on each CC assigned in the set.

Component Carrier Assignment and Configuration: The CC set is the col- lection of CCs where the UE may later-on be scheduled on its PRBs. The assignment and configuration of the CC set to the UEs is a Layer-III functionality in the LTE/LTE-A protocol stack for RRM and happens with RRC signaling to the UEs. The CC configuration functionality is important in optimizing throughput, fairness, power consumption, [4], etc. The QoS requirements, radio channel con- ditions, and UE capability like residual power, SNR, and antenna configuration are taken into account for assigning and configuring the CC set to the UEs.

Fig. 1.8 Radio resource management framework for CA

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The dedicated bearer established between the UE and the core network, corre- sponding to the traffic subscribed by the UE, communicates its QoS requirements in terms of the Guaranteed Bit Rate (GBR), Aggregate Maximum Bit Rate (AMBR), packet delay, loss rate tolerance, etc., indexed by the QoS class identifier [3]. Recalling the illustration above, the GBR traffic (corresponding to an SVC-encoded session’s base layer), identified by a corresponding lower bound, can be assigned onto the primary CC and served for its QoS requirements to the UEs/eMBMS groups, subscribing to it. If the UEs/groups subscribe to a best-effort traffic, limited by an upper bound MBR/AMBR, then both the primary and the optional but dynamically (de-)activated secondary CCs can be invariably used to schedule the subscribed traffic (similar to serving the best-effort enhancement layers up to the MBR). The algorithms for CC assignment and configuration are open issues and are specific to eNB vendors; however, the assignment falls under the following categories:

• Channel-blind CC Assignment: Here, the assignment of the CCs to the UEs is agnostic of the radio channel characteristics of the UEs and is done based on balancing the traffic load served on all CCs in the set, such that each CC approximately serves equal amount of load. Some of the widely adopted tech- niques in a channel-blind assignment [27] include (i) Round-robin balancing, where a newly arriving UE is assigned to the CC that has the least number of UEs as the primary CC, thereby evenly distributing the load across all CCs, and (ii) Mobile hashing, where a hashing algorithm is used at the UE’s end for choosing its primary CC and subsequently, establishing the RRC setup between the eNB and the UE. The output hash values are uniformly distributed among a finite set that maps on to the CC indices, thereby aiming to provide a balanced load across the CCs. The secondary CCs are similarly subsequently chosen for a UE based on its QoS specifications. Channel-blind CC assignment is mostly used for intra-band CA.

• Channel-aware CC assignment: The CC assignment is cognitive of the radio channel characteristics of the UEs and is widely used in inter-band CA. One of the standard techniques employed is a path loss-based CC assignment that accounts for the central band frequency of each CC in the set. Two techniques widely used in a channel-aware CC assignment include (i) throughput-based assignment [13, 28], where the UEs are sorted in decreasing order of their channel access probabilities and are assigned CCs which yield lower path loss values (usually, lower than a pre-defined threshold), and (ii) edge-prioritized CC assignment [4], which is usually done to increase the net system fairness. This sorts UEs in increasing order of their overall channel quality, with the UEs having poorer channel conditions preceding those having stronger channels. The assignment follows a similar path-based approach to UEs in the sorted order. Channel-aware CC assignment, however, needs further optimization, to evenly balance the load across the CCs.

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Downlink Packet Scheduling: Packet scheduling is a dynamic Layer-II RRM functionality at the MAC layer of the LTE/LTE-A protocol stack and is responsible for scheduling UEs across their assigned, configured and activated CCs. It takes care of allocating the PRBs of the activated CCs, corresponding to the primary and sec- ondary cells, to the UEs. In scheduling the PRBs to the UEs, this functionality leverages the multiuser diversity feature which supports multiplexing different UEs with differentMCS values (based on the UEs’ channel quality) on each independently modulated frequency sub-channel of the CC across sub-frames in the time domain. When two ormore UEs are assigned onto a commonCC, the individual PRBsmust be scheduled to the different UEs, so as to avoid resource conflict and contention among UEs. The PRBs yield different rates to different UEs. Scheduling can be done in parallel across each individual CC in the set, including some coordination amongCCs in the set to ensure optimal system performance and joint controlled signaling for UEs assigned onto multiple CCs. LTE-Advanced facilitates cross-CC scheduling which allows the eNB, supporting CA, to send scheduling grants for each UE for data transmissions corresponding to multiple CCs that are assigned to it, on the primary CC. Cross-CC scheduling allows more than one CC to jointly serve any UE, wherein the traffic subscribed by the UE can be scheduled on PRBs from more than one CC. Different types of scheduling techniques [13] include

• Maximum Throughput Scheduling: This scheduling technique is used to achieve the highest-possible spectral efficiency, which results in maximizing the system throughput. The operating principle is to schedule any PRB from any CC to the UE, which reports the largest possible instantaneous wideband achievable throughput on that CC. This scheduling scheme has benefits in terms of cell throughput and spectral efficiency, but comes at the cost of fairness. UEs with poorer channel conditions, especially at the cell edges, either are allocated lower number of PRBs or face resource exhaustion.

• Blind Equal Throughput: This heuristic attempts to yield the same throughput for all UEs, regardless of their channel quality. The operating principle is to schedule any PRB from any CC to the UE which reports the lowest past-achieved throughput on that CC. This scheduling scheme has benefits in terms of cell throughput and spectral efficiency, but comes at the cost of fairness. UEs with poorer channel conditions, especially at the cell edges, either are allocated lower number of PRBs or face resource exhaustion.

• Proportional Fair (PF) Scheduling: This scheduling technique handles the trade-off between the system throughput and fairness [29]. The operating principle is to schedule any PRB from any CC to the UE which reports the maximum value of the ratio of its instantaneous wideband achievable throughput to its past-achieved throughput on that CC. Different scheduling algorithms can be used across the individual CCs aggregated in the set, based on the QoS requirements of the traffic served on each CC. The RRM functionalities in the MAC layer and the PHY layer are specific to each individual CC in the set. LTE-A supports independent transport blocks, link adaptation, and HARQ on a per-CC basis and accordingly implements the scheduler for each CC.

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1.3 Transmission Diversity and Spatial Multiplexing

1.3.1 Transmit Diversity—Definition and Terminologies

Transmission diversity involves the simultaneous transmission of the same identi- cally modulated information-bearing data signals to a UE or an eMBMS group of UEs, originating from two or more independent eNBs operating on the same central band frequency. This requires a tighter coordination among the eNBs, thereby allowing them to synchronously transmit the same data from the synchronized eNBs on the same set of PRBs using the same MCS value. This deployment of synchronized eNBs is called Single-Frequency Network (SFN) [7, 21].

The transmit diversity feature in an SFN addresses the problem of the variable transmission channel quality between the eNBs and the UE. Consider, e.g., a single transmit antenna at each eNB in the SFN and a single receive antenna at any intended UE served by the SFN. If a link between one of the eNBs and the UE/group undergoes a deep fade as a result of poorer channel conditions that affect the transmission, it is compensated by the combined effect of the links between other eNBs and the UE that may yield better received signal strength for the UE. The transmissions of the identical signals from multiple synchronized eNBs on the same PRBs yield a higher SINR as a result of this over-the-air combining effect. This is typically useful in improving the performance of cell-edge UEs in denser eNB deployments, whose channel conditions are marred by higher inter-cell interference. On the other hand, cascading the synchronization on data transmission and resource allocation across a higher number of eNBs would be detrimental if some of them do not contribute significantly to an increase in SINR for an average individual UE. This could amount to wastage of the deployed spectrum resources at such eNBs.

1.3.2 MIMO and Spatial Multiplexing—Definition and Terminologies

The Multiple-Input and Multi-Output (MIMO) technique requires the use of mul- tiple antennae at both the transmitter eNB and receiver UE [30]. MIMO techniques such as beamforming, spatial multiplexing, and spatial diversity play a fundamental role in LTE-Advanced. Thus, in order to maximize the net downlink throughput for a multi-antenna receiver UE, MIMO leverages multi-stream beamforming, which sends multiple streams of UE-subscribed data signals with independent and appropriate precoded weights (based on phase and gain) over the multiple antennae equipped at the transmitter. Beamforming is a signal processing technique for directional transmission of signals by the eNB toward a particular direction in the cell or cell sector served by the eNB [12]. This steering of the beam toward a particular direction helps in increasing the received signal strength of the UEs

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present in that direction, thereby enhancing spatial selectivity. This is done by combining elements in a phased array such that the relative phase and gain (am- plitude) of the signals are controlled at each transmitter antenna so as to enable a constructive interference of the signals at the receiver UE. Let us assume that the eNB has t transmit antennae and the UE has r receive antennae. So, there are t parallel data streams with independently coded phase and gain weights, repre- senting a vector �x of size t. If �y is the received signal vector for the UE over the r antennae, then we have �y ¼ H�xþ n; where H is an r × t complex channel matrix, representing the channel gain values between the eNB and the UE accounting for the multiple antennae equipped at both the eNB and the UE, respectively, and n is the noise vector of size r at the UE. In this case, one of the widely used techniques to combine the received signal vector of size r at the receiver is Minimum Mean-Square error (MMSE) estimator [31].

MIMO-OFDMA: A linear MIMO-OFDM system is a system where the given frequency-selective channel in any CC is divided into a set of fixed parallel flat-fading, independently modulated sub-carriers. A group of 12 sub-carriers are combined to form a sub-channel, and these sub-channels are allocated to UEs for a pair of time slots, and the unit of frequency–time resources to be allocated to every UE is called PRB, as described earlier. This concept is called OFDMA, where different UEs can be simultaneously multiplexed on to the different sub-channels of the same CC with varying MCS rates. The flexibility of using different MCS rates for different UEs across sub-channels of the same CC during the same time is called multiuser (MU) diversity [32]. MIMO-OFDMA supports using different MIMO beamforming vectors simultaneously across sub-channels for scheduling UEs [16]. Figure 1.9 shows two different UEs with different beamforming vector weights from a single eNB, scheduled across 2 PRBs. MIMO-OFDMA is based on an extended version of space division multiple access (SDMA) [12] that allows multiple transmitters to send separate signals and multiple receivers to receive separate signals simultaneously in the same band.

1.3.3 Coordinated Multi-point Transmission

Coordinated multipoint (CoMP) transmission and reception techniques [33] utilize MIMO transmissions from multiple eNBs to enhance the received signal quality as well as decrease the received spatial interference. It is a framework that refers to a system where several geographically distributed antenna nodes on multiple syn- chronized eNBs cooperate with the aim of improving the performance of the users associated with the cells of the eNBs by choice of appropriate beamforming vectors. Figure 1.10 shows how two eNBs can choose appropriate beamforming vectors to serve the two UEs in the network, so as to address inter-cell interference. CoMP leverages both transmission diversity and spatial diversity to multiplex different UEs that support different data rates. It encompasses all required system designs to achieve tight coordination for transmission and reception. It serves two main

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