Cover
Title Page
List of Contributors
Preface
Acknowledgements
1 Introduction
1. 1.1 Introduction
2. References
2 A C‐RAN Approach for 5G Applications
1. 2.1 Introduction
2. 2.2 From Wired to Wireless Backhaul/Fronthaul Technologies
3. 2.3 Architecture for Coordinated Systems According to Baseline 3GPP
4. 2.4 Reference Architecture for C‐RAN
5. 2.5 Potential Applications for C‐RAN‐based Mobile Systems
6. 2.6 Conclusion
7. References
3 Backhauling 5G Small Cells with Massive‐MIMO‐Enabled mmWave Communication
1. 3.1 Introduction
2. 3.2 Existing Wireless Backhauling Solutions for 5G Small Cells
3. 3.3 Fundamentals of mmWave and Massive MIMO Technologies
4. 3.4 MmWave Backhauling: State of the Art and Research Issues
5. 3.5 Case Study: Massive‐MIMO‐based mmWave Backhauling System
6. 3.6 Conclusion
7. Acknowledgement
8. References
4 Fronthaul for a Flexible Centralization in Cloud Radio Access Networks
1. 4.1 Introduction
2. 4.2 Radio Access Network Architecture
3. 4.3 Functional Split Options
4. 4.4 Requirements of Flexible Functional Splits
5. 4.5 Statistical Multiplexing in a Flexibly Centralized Network
6. 4.6 Convergence of Fronthaul and Backhaul Technologies
7. 4.7 Enablers of a Flexible Functional Split
8. 4.8 Summary
9. Acknowledgement
10. References
5 Analysis and Optimization for Heterogeneous Backhaul Technologies
1. 5.1 Introduction
2. 5.2 Backhaul Model
3. 5.3 Backhaul Packet Delay Analysis
4. 5.4 Backhaul Deployment Cost Analysis
5. 5.5 Backhaul‐aware BS Association Policy
6. 5.6 Conclusions
7. References
6 Dynamic Enhanced Inter‐cell Interference Coordination Strategy with Quality of Service Guarantees for Heterogeneous Networks
1. 6.1 Introduction
2. 6.2 System Model and Problem Statement
3. 6.3 Dynamic Interference Coordination Strategy
4. 6.4 Numerical Results
5. 6.5 Conclusion
6. References
7 Cell Selection for Joint Optimization of the Radio Access and Backhaul in Heterogeneous Cellular Networks
1. 7.1 Introduction
2. 7.2 System Model and Problem Statement
3. 7.3 Proposed Solutions
4. 7.4 Simulation Results
5. 7.5 Conclusion
6. References
8 Multiband and Multichannel Aggregation for High‐speed Wireless Backhaul
1. 8.1 Introduction
2. 8.2 Spectrum for Wireless Backhaul
3. 8.3 Multiband and Multichannel Aggregation
4. 8.4 Spectrally Efficient Channel Aggregation
5. 8.5 Practical System Examples
6. 8.6 Conclusions
7. References
9 Security Challenges for Cloud Radio Access Networks
1. 9.1 Introduction
2. 9.2 Overview of C‐RAN Architecture
3. 9.3 Intrusion Attacks in the C‐RAN Environment
4. 9.4 Distributed Denial of Service (DDoS) Attacks Against C‐RAN
5. 9.5 Conclusions
6. References
Index
End User License Agreement

List of Tables

Chapter 02
1. Table 2.1 Values of LCID for DL‐SCH (adapted from [19])
2. Table 2.2 Values of LCID for UL‐SCH (adapted from [19])
3. Table 2.3 Simulation parameters
4. Table 2.4 Comparison of technologies
Chapter 03
1. Table 3.1 Probability distribution function of
Chapter 04
1. Table 4.1 Parameters impacting FH requirements for different functional splits
2. Table 4.2 Parameters for the calculation of data rate requirements
3. Table 4.3 Delay requirements of 4G and exemplary 5G systems for split A
Chapter 05
1. Table 5.1 Parameter settings. Reproduced from [43] with permission from the IEEE
Chapter 06
1. Table 6.1 Parameter settings
Chapter 07
1. Table 7.1 and with respect to different resource‐allocation policies
2. Table 7.2 Main simulation parameters

List of Illustrations

Chapter 01
1. Figure 1.1 Communication haul segments of (a) legacy and (b) emerging C‐RAN mobile network
Chapter 02
1. Figure 2.1 Different types of backhaul/fronthaul
2. Figure 2.2 Network architecture of baseline 3GPP CoMP system
3. Figure 2.3 Depiction of a JT CoMP use case
4. Figure 2.4 Protocol architecture of baseline 3GPP CoMP system
5. Figure 2.5 Operator’s perspective on the fronthaul‐based C‐RAN scenario
6. Figure 2.6 Architecture of the cloud resource optimizer
7. Figure 2.7 Unified MAC frame for C‐RAN
8. Figure 2.8 On‐demand, device‐centric advanced C‐RAN
9. Figure 2.9 Throughput vs. ideal/non‐ideal fronthaul
Chapter 03
1. Figure 3.1 Small‐cell network architecture incorporating massive‐MIMO‐based mmWave backhauling system
2. Figure 3.2 SINR coverage probability for mmWave backhaul link, mmWave and microwave access link
3. Figure 3.3 Average user rate for (a) mmWave and (b) microwave access link with different backhaul quotas (Q_h)
4. Figure 3.4 (a) Network rate and (b) average user rate for different user‐association schemes
Chapter 04
1. Figure 4.1 System architecture and protocol stack of decentralized, flexible and centralized networks
2. Figure 4.2 Functional split options.
3. Figure 4.3 Data rate requirements of different functional splits for current and future networks
4. Figure 4.4 Cumulative distribution function (CDF) of the data rate for different functional split options with full load (solid lines) and an exemplary variable load (dashed lines).
5. Figure 4.5 PDFs of aggregated FH traffic of one, two, four and eight BSs, and data rate percentiles
6. Figure 4.6 Number of supported BSs versus deployed FH capacity of different functional split options with and without multiplexing gain (solid and dashed lines, respectively).
7. Figure 4.7 Number of supported BSs versus deployed FH capacity of split option D for uniform load distributions with different variances
8. Figure 4.8 Functional split requirements versus physical layer technologies.
9. Figure 4.9 Number of supported BSs for different aggregation technologies: xDSL at 200 Mbps; μ‐wave at 2 Gbps; fibre at 20 Gbps.
Chapter 05
1. Figure 5.1 Heterogeneous network model embracing various backhaul technologies.
2. Figure 5.2 Variations in conditional mean packet delay of backhaul links with system parameters.
3. Figure 5.3 Delay‐limited success probability versus system parameters.
4. Figure 5.4 Variation in mean backhaul cost per small‐cell BS with gateway density.
5. Figure 5.5 Effect of (a) small‐cell BS and (b) gateway densities on mean network packet delay.
6. Figure 5.6 Effect of bias factor on mean network packet delay under different spectrum modes: (a) spectrum‐partition mode; (b) spectrum‐sharing mode.
7. Figure 5.7 Effect of small‐cell BS density on BS association. (a) Mean network packet delay; (b) small‐cell association probability; (c) normalized small‐cell bias factor.
8. Figure 5.8 Effect of gateway density on BS association. (a) Mean network packet delay; (b) small‐cell association probability; (c) normalized small‐cell bias factor.
Chapter 06
1. Figure 6.1 Multi‐tiered cellular communication network
2. Figure 6.2 INC UEs and CRE UEs in small cells
3. Figure 6.3 Simulation environment set‐up
4. Figure 6.4 Comparison of dynamic and static eICIC sum rates using the sLRB model in Scenario 1
5. Figure 6.5 Comparison of dynamic and static eICIC sum rates using the sLRB model in Scenario 2
6. Figure 6.6 Comparison of different biases versus the number of used RBs in the sLRB model
7. Figure 6.7 Comparison of user sum rates with and without QoS in the sLRB model
8. Figure 6.8 User‐blocking probability with QoS in the sLRB model
9. Figure 6.9 Comparison of user sum rates with and without QoS in the HotSpot model
10. Figure 6.10 User‐blocking probability with QoS in the HotSpot model
11. Figure 6.11 Comparison of average user rates with and without QoS in the sLRB model
12. Figure 6.12 Comparison of average small‐cell user rates with and without QoS in the sLRB model
13. Figure 6.13 Comparison of dynamic and static eICIC sum‐log utilities using the sLRB model in Scenario 1
14. Figure 6.14 Comparison of dynamic and static eICIC sum‐log utilities using the sLRB model in Scenario 2
15. Figure 6.15 Comparison of different biases for the sum rate of the modified sum‐rate utility and modified sum‐log utility in the sLRB model
16. Figure 6.16 Comparison of different biases for the fairness of the modified sum‐rate utility and modified sum‐log utility in the sLRB model
Chapter 07
1. Figure 7.1 The heterogeneous network deployment under investigation. F1 and F2 are the carrier frequencies for the macro layer and the small‐cell layer, respectively [14]
2. Figure 7.2 Evolve paradigm for managing user–eNB association
3. Figure 7.3 The bipartite graph G used to model the cell‐association problem
4. Figure 7.4 The required message passing in the proposed algorithms
5. Figure 7.5 Cumulative distribution function of the network capacity achieved with different association schemes with respect to the backhaul capacity. (a) C ^BH = 40 Mbps; (b) C ^BH = 80 Mbps; (c) C ^BH = 120 Mbps
6. Figure 7.6 A snapshot of the 3 × 3 SCeNB clusters in the central macro cell
7. Figure 7.7 A snapshot of the association pattern when using as association metric the strength of the downlink signal (a) and the aggregated network load (b)
8. Figure 7.8 Convergence of the Evolve algorithm with respect to different backhaul constraints and resource‐allocation policies
9. Figure 7.9 Cumulative distribution function of the uplink transmission power at UEs with different backhaul constraints. (a) C ^BH = 40 Mbit/s MCI; (b) C ^BH = 80 Mbit/s MCI; (c) C ^BH = 120 Mbit/s MCI
Chapter 08
1. Figure 8.1 General microwave radio frequency band plan [7]
2. Figure 8.2 E‐band channel plan for 10 GHz TR spacing (a) and less than 5 GHz TR spacing (b) [8]
3. Figure 8.3 Point‐to‐point link using multiband ASB transceivers
4. Figure 8.4 Multiband ASB transceiver architecture
5. Figure 8.5 Forward path transmitter
6. Figure 8.6 Return path receiver
7. Figure 8.7 Illustration of different methods of subband aggregation
8. Figure 8.8 RF signal generation using a direct single‐carrier modulation approach
9. Figure 8.9 Transmitter block diagram using ASB‐FDMA
10. Figure 8.10 Receiver block diagram using ASB‐FDMA
11. Figure 8.11 RF signal generation using the frequency‐domain multicarrier modulation approach
12. Figure 8.12 Transmitter block diagram using ASB‐OFDM
13. Figure 8.13 Receiver block diagram using ASB‐OFDM
14. Figure 8.14 Full multiband, multichannel approach
15. Figure 8.15 Block diagram of spectrally efficient channel aggregation system
16. Figure 8.16 Frequency‐domain multiplexing without a guard band (a) for transmitter and (b) demultiplexing for the receiver; (c) the full spectrum at IF
17. Figure 8.17 Digital IF signal generated at (a) transmitter and received at (b) receiver based on OFDM transmission
18. Figure 8.18 CSIRO Ngara microwave backhaul transceiver block diagram
19. Figure 8.19 CSIRO 6 Gbps E‐band backhaul transceiver block diagram
Chapter 09
1. Figure 9.1 C‐RAN architecture
2. Figure 9.2 Vulnerabilities and entry points for intrusion attacks in a C‐RAN environment
3. Figure 9.3 C‐RAN architecture under potential attacks in a 5G communication scenario

Preface

In a mobile communication system, the segment that connects the core to the access networks is termed the ‘backhaul’. The edges of any telecommunication network are connected through backhauling. The importance of backhaul research is spurred by the need for increasing data capacity and coverage to cater for the ever‐growing population of electronic devices – smartphones, tablets and laptops – which is foreseen to hit unprecedented levels by 2020. The backhaul is anticipated to play a critical role in handling large volumes of traffic, its handling capability driven by stringent demands from both mobile broadband and the introduction of heterogeneous networks (HetNets). Backhaul technology has been extensively investigated for legacy mobile systems, but is still a topic that will dominate the research arena for next generation mobile systems; it is clear that without proper backhauling, the benefits introduced by any new radio access network technologies and protocols would be overshadowed.

Traditionally, the backhaul segment connects the RAN (radio access network) to the rest of the network where the baseband processing takes place at the cell site. However, with the onset of next generation networks, the notion of ‘fronthaul access’ is also gaining momentum. The future technology roadmap points towards SDN (software‐defined networks) and network virtualization as means of effectively sharing resources on demand between different mobile operators, thus taking a step towards reducing the operational and capital expenditure in future networks. Moreover, the baseband processing will be centralized, allowing the operators to completely manage interference through coordinated resource‐management strategies. In fact, 3GPP are today visualizing a C‐RAN (cloud‐RAN) architecture, where the evolved base stations are connected to the C‐RAN unit through communication hauls, to what is referred to as the ‘fronthaul network’. Traditionally, fibre‐optic technology is used to roll out the deployment of base stations; however, this comes along with inherent limitations, including cost and lack of availability at many small sites. This provides the impetus for radio solutions that can handle large volumes of traffic on the fronthaul access, triggering the research community at large to find alternative and advanced solutions that can supersede fibre.

The current work on backhaul and fronthaul technology is fragmented, and still in its infancy. There are still giant steps to be taken towards developing concrete solutions to provide a modern communication haul for next generation networks, which is also commonly referred to as 5G. This book aims to be the first of its kind to hinge together the related discussions on the fronthaul and backhaul access under the umbrella of 5G networks, which we will often refer to as the ‘communication haul’. We aim to discuss these pivotal building blocks of the communication infrastructure and provide a view of where it all started, where we are now in terms of LTE/LTE‐A networking and the future challenges that lie ahead for 5G. In addition, this book presents a comprehensive analysis of different types of backhaul/fronthaul technologies while introducing innovative protocol architectures.

In the compilation of this book, the editors have drawn on their vast experience in international research and being at the forefront of the communication haul research arena and standardization. This book aims to be the first to talk openly about next generation communication hauls, and will hopefully serve as a useful reference not only for postgraduate students to learn more about this evolving field, but also to stimulate mobile communication researchers towards taking further innovative strides in this field and marking their legacy in the 5G arena.

Kazi Mohammed Saidul Huq
Jonathan Rodríguez

Instituto de Telecomunicações, Aveiro, Portugal

1
Introduction: The Communication Haul Challenge

Kazi Mohammed Saidul Huq and Jonathan Rodriguez

Instituto de Telecomunicações, Aveiro, Portugal

1.1 Introduction

Nowadays, the mobile Internet is a pervasive phenomenon that is changing social trends and playing a pivotal role in creating a digital economy. This, in part, is driven by advancements in semiconductor technology, which are enabling faster and more energy‐compliant devices, such as smartphones, tablets and sensor devices, among others. However, a truly smart digital world is still in its infancy and the current trends are set to continue, leading to an unprecedented rise in mobile data traffic and intelligent devices. In fact, according to an Ericsson report [1], a typical laptop will generate 11 GB, a tablet 3.1 GB and a smartphone 2 GB per month by the end of 2018. These figures represent the changing communication paradigm, where the end user will not only receive data but generate data; in other words, the end user will become a ‘prosumer’ running data‐hungry applications, for example, high‐definition wireless video streaming, machine‐to‐machine communication, health‐monitoring applications and social networking. Therefore, existing technology requires a radical engineering design upgrade in order to compete with ever‐growing user expectations and to accommodate the foreseen increase in traffic. The change will be driven by market expectations, and the new technology being considered is fifth generation (5G) communications [2].

Experts anticipate that 5G will deliver and meet the expectations of a new era in wireless connectivity, and will play a key role in enabling this so‐called digital world. In contrast to legacy fourth generation (4G) systems, the widely accepted consensus on the 5G requirement includes [3, 4]:

Capacity: 1000x increase in area capacity;
Latency: Less than 1 millisecond (ms) round trip time (RTT) latency;
Energy: 100x improvement in energy efficiency in terms of Joules/bit;
Cost: 10–100x reduction in cost of deployment;
Mobility: Mobility support and always‐on connectivity of users that have high throughput requirements.

To achieve these targets, all the key mobile stakeholders, such as operators, vendors and the mobile research community, are contriving to reengineer the mobile architecture in order to support higher‐speed data connectivity.

Small‐cell technology is an emerging deployment that is providing promising results in terms of delivering fast connectivity due to the small distance between the base station (BS) and the end user, whilst reducing energy consumption. Market use cases of small cells such as the indoor femto cell have already become a success story, so the question is, can we extrapolate the femto cell paradigm to the outdoor world? In fact, current trends are suggesting that this is the way forward, with multi‐tier heterogeneous networks being a new design addition to the LTE‐Advanced standard [5, 6]. Here, multi‐tier radio networks (small‐cell tiers) play a pivotal role, coupled with network coexistence approaches to reduce the interference between tiers. Moreover, mobile technology will continue to evolve in this direction with the hyper‐dense deployment of small cells providing hotspot islands of high data connectivity coverage zones. This context will ask new questions from the research community in terms of how to tunnel this traffic from the local serving base station towards the core network. Typically, in legacy networks, the segment of the network that interconnects the BS to the RAN (radio access network) to the EPC (evolved packet core) is called the backhaul. Fibre optic lines or microwave links have fulfilled this role, with limitations in terms of deployment cost and limited coverage area. However, mobile technology is heading towards an era of virtualization and software‐defined networking, where radio resources are allocated from a common pool to different providers, and their management is centralized. This new era is, in fact, reflecting parallels in the cloud computing world, with the onset of cloud services. Emerging mobile networks are heading towards a C‐RAN (cloud radio access network) approach [7, 8], where RRUs (remote radio units) and a centralized processing RAN core work in synergy to provide coordinated scheduling, or, in other words, interference management. This paradigm is changing the perception of the communication haul in the network, from backhauling to incorporating both a back and fronthaul segment. In this context, the backhaul dictates how the information is parried from the base stations to the core network, whilst the fronthaul refers to the connectivity segment between the C‐RAN core network and the small cell. Figure 1.1 shows definitions of the backhaul and fronthaul segments pertaining to legacy and emerging C‐RAN architectures.

Schematics of communication haul segments of legacy (top) and emerging (bottom) C-RAN mobile network with aggregation point and BBU pool cloud, respectively, prior to core network. — **Figure 1.1** Communication haul segments of (a) legacy and (b) emerging C‐RAN mobile network

The future enhanced communication haul (be it backhaul or fronthaul) for 5G is expected to be deployed around 2020 in order to support the exponential growth in wireless data that is forecast over the next decade. Therefore, there is substantial market interest in the development of ground‐breaking backhaul and fronthaul solutions that can not only enhance today’s networks, but also provide a coherent interference management approach in emerging technologies such as C‐RAN and beyond. This communication haul challenge provided the inspiration for this book and its title: Backhauling/Fronthauling for Future Wireless Systems.

The book intends to bring together all mobile stakeholders, from academia and industry, to identify and promote technical challenges and recent results related to smart backhaul/fronthaul research for future communication systems such as 5G. It provides an overview of current approaches to backhauling legacy communication systems and explains the rationale for deploying future smart and efficient backhauling/fronthauling infrastructure from architectural, technical and business points of view using real‐life applications and use cases. The book is intended to inspire researchers, operators and manufacturers to render ground‐breaking ideas in the newly emerging discipline of smart backhauling/fronthauling over future, ultra‐dense wireless systems. Moreover, detailed security challenges are presented to analyse the performance of smart backhauling/fronthauling for future wireless. It is clear that smart backhauling/fronthauling deployment can offer a palette of interesting colours capable of painting new business opportunities for mobile stakeholders for next generation wireless communication systems. This is the first book of its kind on smart backhauling/fronthauling for future wireless systems which updates the research community on the communication haul roadmap, reflecting current and emerging features emanating from the 3GPP group.

To guide the reader through this adventure, the book has the following layout. In Chapter 2, a reference architecture for the future radio communication haul is presented from a 5G perspective. 5G networks are anticipated to obtain Shannon‐level and beyond throughput and almost zero latency. However, there are several challenges to solve if 5G is to outperform legacy mobile platforms; one of these is the design of the communication ‘haul’. Traditionally, the backhaul segment connects the radio access network (RAN) to the rest of the network where the baseband processing takes place at the cell site. However, in this chapter, we will use the concept of ‘fronthaul access,’ which is recently gaining significant interest since it has the potential to support remote baseband processing based on adopting a cloud radio access network (C‐RAN) architecture that aims to mitigate (or coordinate) interference in operator‐deployed infrastructures; this eases significantly the requirements in interference‐aware transceivers. To do this, we provide a reference architecture that also includes a network and protocol architecture and proposes a so‐called ‘cloud resource optimizer’. This integrated solution will be the enabler for RAN‐as‐a‐Service, not only paving the way for effective radio resource management, but opening up new business opportunities for virtual mobile service providers.

Emerging channel transmission approaches and the possibility of using higher frequency bands, such as massive MIMO and millimetre‐wave (mmWave), respectively, are of paramount importance for future wireless systems and for the communication haul. Chapter 3 introduces the fundamentals with regard to massive MIMO and mmWave communication, and their suitability for small‐cell backhauling and fronthauling. Furthermore, a performance analysis model for wireless backhauling of small cells with massive MIMO and mmWave communication is outlined. Using this model, some numerical results on the performance of massive‐MIMO‐ and/or mmWave‐based wireless backhaul networks are presented.

C‐RAN promises considerable benefits compared to decentralized network architectures. Centralizing the baseband processing enables smaller radio access points as well as cooperative signal processing and ease of upgrade and maintenance. Further, by realizing the processing not on dedicated hardware, but on dynamic and flexible general‐purpose processors, cloud‐based networks enable load balancing between processing elements to enhance energy and cost efficiency. However, centralization also places challenging requirements on the fronthaul network in terms of latency and data rate. This is especially critical if a heterogeneous fronthaul is considered, consisting not only of dedicated fibre but also of, for example, mmWave links. A flexible centralization approach can relax these requirements by adaptively assigning different parts of the processing chain either to the centralized baseband processors or the base stations based on the load situation, user scenario and the availability of the fronthaul links. This not only reduces the requirements in terms of latency and data rate, but also couples the data rate to the actual user traffic. In Chapter 4, a comprehensive overview of different decentralization approaches is given, and we analyse their specific requirements in terms of latency and data rate. Furthermore, we demonstrate the performance of flexible centralization and provide design guidelines on how to set up the fronthaul network to avoid over‐ or under‐dimensioning.

Heterogeneous backhaul deployment using different wired and wireless technologies is a potential solution to meet the demand in small‐cell and ultra‐dense networks. Therefore, it is of cardinal importance to evaluate and compare the performance characteristics of various backhaul technologies in order to understand their effect on the network aggregate performance and provide guidelines for system design. In Chapter 5, the authors propose relevant backhaul models and study the delay performance of various backhaul technologies with different capabilities and characteristics, including fibre, xDSL, mmWave and sub‐6 GHz. Using these models, the authors aim to optimize the base station (BS) association so as to minimize the mean network packet delay in a macro‐cell network overlaid with small cells. Furthermore, the authors model and analyse the backhaul deployment cost and show that there exists an optimal gateway density that minimizes the mean backhaul cost per small‐cell base station. Numerical results are presented to show the delay performance characteristics of different backhaul solutions. Comparisons between the proposed and traditional BS association policies show the significant effects of backhaul on network performance, which demonstrates the importance of joint system design and optimization for both the radio access and backhaul networks.

The small‐cell network (also called a HetNet) has been recognized as a potential solution to offer better service coverage and higher spectral efficiency. However, the dense deployment of small cells could cause inter‐cell interference problems and reduce the performance gains of HetNets. Various techniques have been developed in 4G for tackling inter‐cell interference. In particular, the inter‐cell interference coordination (ICIC) technique can coordinate the data transmission and interference in two neighbouring cells. In Chapter 6, the authors consider a HetNet consisting of macro‐cell networks overlaid with small‐cell networks that access the same spectrum simultaneously. Here, the HetNet architecture assumes macro cells and small cells interconnected via a high‐speed fronthaul/backhaul connection. In particular, due to the mobility of wireless subscribers, the load and data traffic are different in every active macro and small cell. The conventional static enhanced ICIC (eICIC) mechanism cannot ensure that adapting the almost blank subframes (ABS) duty cycle corresponds to the dynamic network condition. Only the dynamic eICIC mechanism is suitable for this non‐static network traffic. Therefore, the authors aim to develop a dynamic interference coordination strategy for eICIC for maximizing system utilities under given QoS constraints. In contrast to the traditional eICIC mechanism, the proposed method does not add any backhaul requirements. Computer simulations show that the performance in various scenarios of the dynamic eICIC mechanism with QoS requirements is better than a static eICIC approach and the conventional dynamic eICIC mechanism.

Cell selection for joint optimization considering backhauling technology is needed for future wireless systems. In this regard, Chapter 7 provides a comprehensive analysis for joint optimization considering the backhaul in terms of cell selection. This chapter considers heterogeneous cellular networks, where clusters of small cells are locally deployed to create hotspot regions inside the macro‐cell area. Most of the research on this topic has focused on mitigating co‐channel interference; however, the wireless backhaul has recently emerged as an urgent challenge to enable ubiquitous broadband wireless services in small cells. In realistic scenarios, the backhaul may limit the amount of signalling that can be exchanged amongst neighbouring cells, which aims to coordinate their operations in real time; furthermore, in highly loaded cells (such as hotspots), the backhaul can limit the data rate experienced by the end users. Here, the authors develop a novel cell‐association framework, which aims to balance the users amongst heterogeneous cells to improve the overall radio and backhaul resource usage and increase the system performance. The authors describe the relationship between cell load, resource management and backhaul capacity constraints. Then, the cell‐selection problem is expressed as a combinatorial optimization problem and two heuristic algorithms – called Evolve and Relax – are presented to solve this dilemma. The analysis shows that Evolve converges to a near‐optimal solution, leading to notable improvements with respect to the classic SINR‐based association scheme in terms of throughput and resource utilization efficiency.

High‐speed and long‐range wireless backhaul is a cost‐effective alternative to a fibre network. The ever‐increasing demand for high‐speed broadband services mandates higher spectral efficiency and wider bandwidth to be adopted in the wireless backhauls. As wireless mobile networks evolve toward 5G, employing higher‐order modulation and performing multiband and multichannel aggregation for wireless backhauling have become industry trends. However, commercially available wireless backhaul systems do not meet the stringent requirements for both high speed and long range at the same time. In Chapter 8, the various system architectures for multiband and multichannel aggregation are discussed. The challenges for achieving high‐speed wireless transmission in multiband and multichannel systems are addressed. These challenges include: how to improve spectrum efficiency and power efficiency; how to prevent inter‐channel interference; and how to ensure low latency in order to ensure resilient packet delivery and load balancing.

Despite the significant benefits of C‐RAN technology in 5G mobile communication systems, C‐RAN technology has to face multiple inherent security challenges associated with virtual systems and cloud computing technology, which may hinder its successful establishment in the market. Thus, it is critical to address these challenges in order for C‐RAN technology to reach its full potential and foster the deployment of future 5G mobile communication systems. Therefore, Chapter 9 presents representative examples of possible threats and attacks against the main components in the C‐RAN architecture in order to shed light on the security challenges of C‐RAN technology and provide a roadmap to overcome the security bottleneck.

In conclusion, we firmly believe this book will serve as a useful reference for early‐stage researchers and academics embarking on this radio communication haul odyssey, but beyond that, it targets all major 5G stakeholders who are working at the forefront of this technology to provide inspiration towards rendering ground‐breaking ideas in the design of new communication hauls for next‐generation systems.

References

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