Chapter
Part I: Physical Layer for 5G Radio Interface Technologies
Chapter 1: Emerging Technologies in Software, Hardware, and Management Aspects Toward the 5G Era: Trends and Challenges
1.2 5G Requirements and Technology Trends
1.3 Status and Challenges in Hardware and Software Development
1.3.2.1 Functions Definition (LTE, 3GPP-Based PHY Functions)
1.3.2.2 Parameters (KPIs)/ Constraints Definition
1.3.2.3 Functional Graph (Dataflow Graph) Provision
1.3.3 Optimization Problem Formulation
1.3.4 Evolutionary Multiobjective Algorithmic Solution
1.3.6 Preliminary Test Results
1.3.7 Status and Challenges in 5G Wireless Communications
1.3.7.1 Novel Physical Layer Aspects
1.3.7.2 Novel Frame Design Based on Service Requirements
1.3.7.3 Support of Different Numerologies
1.3.8 Enhanced Radio Resource Management (RRM) and MAC Adaptation for 5G
1.4 5G Network Management Aspects Enhanced with Machine Learning
1.4.1 Machine Learning for Service Classification in 5G Networks
1.4.2 State-of-the-ArtMachine Learning Mechanisms for Traffic Classification
1.4.3 Classification Approach and Evaluation Metrics
1.4.4 Evaluation Performance of Classification Mechanisms
Chapter 2: Waveform Design for 5G and Beyond
2.2 Fundamentals of the 5G Waveform Design
2.2.1 Waveform Definition
2.2.2 5G Use Cases and Waveform Design Requirements
2.2.3 The Baseline for 5G Waveform Discussion: CP-OFDM
2.3 Major Waveform Candidates for 5G and Beyond
2.3.1 Multicarrier Schemes
2.3.1.2 Subcarrier-Wise Filtering
2.3.1.3 Subband-Wise Filtered MCM
2.3.2 Single-Carrier Schemes
Chapter 3: Full-Duplex System Design for 5G Access
3.2 Self-Interference Cancellation
3.2.1 General SIC Architectures
3.2.2 Self-Interference Cancellation State of the Art
3.3 FD System Design: Opportunities and Challenges
3.3.1 New Interferences in FD Systems
3.3.1.1 BS-to-BS Interference
3.3.1.2 UE-to-UE Interference
3.3.2 Efficient Interference Measurement
3.3.3 Complexity and Latency Consideration
3.4 Designing the FD System
3.4.1 Overall Design for FD
3.4.2 Design to Mitigate BS-to-BS Interference
3.4.2.1 Elevation Beam Nulling
3.4.2.2 Uplink Power Control
3.4.3 Design to Mitigate UE-to-UE Interference
3.4.3.1 Joint Downlink–Uplink Scheduler
3.4.3.2 Channel Quality Indicator Feedback for Joint Scheduling
3.4.3.3 Interference Measurement and Reference Signal Design
3.5 System-Level Performance Analysis
3.5.1 General Simulation Methodology and Assumptions
3.5.1.1 Deployment Models
3.5.2 Performance of BS-to-BS Interference Mitigation Schemes
3.5.3 System Performance for Schemes to Treat UE-to-UE Interference
3.5.3.1 System Performance of Joint Scheduler
3.5.3.2 Performance of Various CQI Feedbacks
3.5.4 System Performance for Various Operation Regimes
3.5.4.1 Performance for Various UE Densities and Bundle Scheduler
3.5.4.2 Performance of Various LPN Densities
3.6 Conclusions and Future Directions
3.6.1 Improvement to the Current Design
3.6.1.1 Intercell UE-to-UE Interference Handling
3.6.1.2 Traffic Asymmetry
3.6.2 More Scenarios and Future Work
3.6.2.1 Full-Duplex Self Backhaul (Relay)
3.6.2.2 Full-DuplexWi-Fi System
3.6.2.3 Full-Duplex Application in LAA
Chapter 4: Nonorthogonal Multiple Access for 5G
4.2 Basic Principles and Advantages of NOMA
4.2.1 Channel Capacity Comparison of OMA and NOMA
4.2.2 Advantages of NOMA Compared to OMA
4.3.1 Basic NOMA Relying on a SIC Receiver
4.3.2 NOMA in MIMO Systems
4.3.5 User Grouping and Resource Allocation
4.3.6 mmWave Communications and Power-Domain NOMA
4.3.7 Application of Power-Domain NOMA
4.4.1 Low-Density Spreading CDMA (LDS-CDMA)
4.4.2 Low-Density Spreading-Aided OFDM (LDS-OFDM)
4.4.3 Sparse Code Multiple Access
4.4.4 Multi User Shared Access
4.4.5 Successive Interference Cancellation Aided Multiple Access (SAMA)
4.5.1 Spatial Division Multiple Access
4.5.2 Pattern Division Multiple Access
4.5.3 Signature-Based NOMA
4.5.4 Interleaver-Based NOMA
4.5.5 Spreading-Based NOMA
4.5.6 Bit Division Multiplexing
4.5.8 Miscellaneous NOMA Schemes
4.6 Comparison and Trade-Off Analysis of NOMA Solutions
4.7 Performance Evaluations and Transmission Experiments of NOMA
4.8 Opportunities and Future Research Trends
Chapter 5: Code Design for Multiuser MIMO
5.2 Multiuser Repetition-Aided IRA Coding Scheme
5.3 Iterative Decoding and EXIT Analysis
5.3.3 Turbo-Like Decoding
5.3.4 Decoding Complexity Computation
5.4 Code Optimization Procedure
5.5 Numerical Results and Comparisons
5.5.2 Rayleigh Fading Channel
Chapter 6: Physical Layer Techniques for 5G Wireless Security
6.1.1 Information Theoretic Security
6.1.2 Organization of the Chapter
6.2 5G Physical Layer Architecture
6.2.1 Full-Duplex Communications
6.2.2 Security in Full-Duplex Communications
6.2.3.1 Bidirectional Topology
6.2.3.2 Base Station Topology
6.3 Secure Full-Duplex Receiver Jamming
6.3.2 Transmit and Receive Designs for SI Cancellation and Jamming
6.3.3 Results and Discussion
6.4 Secure Full-Duplex Bidirectional Communications
6.4.2 Optimization for Secure Bidirectional Communications
6.4.3 Results and Discussion
6.5 Secure Full-Duplex Relay Communications
6.5.2 Proposed Optimization Solution
6.5.3 Results and Discussion
6.6 Future Directions and Open Issues
Chapter 7: Codebook-Based Beamforming Protocols for 5G Millimeter Wave Communications
7.2 Beamforming Architecture
7.2.2 Digital Beamforming
7.3 Beam Searching Algorithm
7.3.1 IEEE 802.15.3c Beam Searching
7.3.2 IEEE 802.11.ad Beam Searching
7.3.3 Hierarchical Beam Searching
7.4.1 IEEE 802.15.3c Codebook
7.4.2 N-Phase Beamforming
7.4.3 DFT-Based Beamforming
7.4.4 Fourier Series Method with Kaiser Window (FSM-KW) Beamforming
7.5 Beamforming Evaluation
Part II: Radio Access Technology for 5G Networks
Chapter 8: Universal Access in 5G Networks: Potential Challenges and Opportunities for Urban and Rural Environments
8.2 Access for Urban Environments
8.2.2 Millimeter Wave Technologies
8.2.2.1 Introduction and Background
8.2.2.2 Analysis of mmWave Communication
8.2.2.3 mmWave as a New Paradigm in Communications
8.3 Providing Access to Rural Areas
8.3.1 Why Traditional Approaches Do Not Work for Rural Areas?
8.3.2 Motivation for Aiming at Coverage in Rural Areas through 5G
8.3.3 5G Technologies Thrusts and Universal Coverage
8.3.4 Backhaul/Access Solutions for Rural Areas
8.3.4.1 Terrestrial 5G Backhaul Solutions
8.3.4.2 Airborne 5G Backhaul Solutions
8.3.4.3 Joint Optimization of Access and Backhaul
8.3.4.4 Application-Specific Design for Rural Coverage
8.3.5 Cost-Effective Solutions to Enable Rural 5G
8.3.5.1 How to Reduce CAPEX
8.3.5.2 How to Reduce OPEX
8.3.5.3 How to Jointly Optimize the CAPEX and OPEX
8.3.5.4 Use of Self-Organized Networking for Rural Coverage
Chapter 9: Network Slicing for 5G Networks
9.2 End-to-End Network Slicing
9.2.1 Architecture for End-to-End Network Slicing
9.2.2 Deployment of Virtual Infrastructure
9.2.3 Deployment of Network Services
9.2.4 E2E Network Slicing Implementations
9.3.1 Management and Orchestration Architecture
9.3.2 Network Slicing MANO Tasks
9.3.3 Run Time Management of Network Slices
9.3.3.1 Generic QoS/QoE Slice MANO Algorithm
9.4 Network Slicing at the Mobile Edge
9.4.1 Enabling Solutions for Mobile Edge Slicing
9.4.2 Slice Requests Brokering
9.4.3 Managing Mobile Edge Slice Resources
9.5 Network Slicing at the Mobile Transport
9.5.1 Enabling Mobile Transport Slicing Technologies
9.5.2 Enabling Slicing Technologies for the Crosshaul MANO
9.5.3 Multi-tenancy Application for Slice Management and Orchestration
9.6 Network Slicing at the Mobile Cloud
9.6.1 Control Plane Modularization to Support Network Slicing
9.6.2 User Plane Simplification for Lean Packet Slices
Chapter 10: The Evolution Toward Ethernet-Based Converged 5G RAN
10.1 Introduction to RAN Transport Network
10.1.4 Network Synchronization and Latency in RAN
10.2 Evolving RAN Toward 5G Requirements
10.2.1 New Radio Functional Splits
10.2.2 New RAN Network Architecture
10.2.3 5G RAN Migration Concerns
10.2.4 Low-Latency Applications and Edge Computing
10.3 Ethernet-Based 5G RAN
10.3.1 Ethernet Tools for Time-Sensitive Networking
10.3.2 NGFI and XHaul Deployment and Implementation Considerations
10.3.3 Radio over Ethernet
10.3.4 Next-Generation Ethernet-Based Base Stations
Chapter 11: Energy-Efficient 5G Networks Using Joint Energy Harvesting and Scheduling
11.2.1 Base Station Power Model
11.2.2 Energy Harvesting Model
11.3 Problem Formulation and Solution
11.3.1 Zero Knowledge Case
11.3.2 Perfect Knowledge Case
11.4 Low-Complexity Algorithm
11.4.1 Binary Particle Swarm Optimization (BPSO)
11.4.2 Genetic Algorithm (GA)
11.6.2 Possible Future Works
Part III: 5G Network Interworking and Core Network Advancements
Chapter 12: Characterizing and Learning the Mobile Data Traffic in Cellular Network
12.1 Understanding the Traffic Nature: A Revisiting to α-StableModels
12.1.1 MIM Working Mechanisms and Dataset Description
12.1.2 Background on α-StableModels
12.1.3 The Statistical Pattern and Inherited Methodology of MIM Services
12.1.3.2 Aggregated Traffic
12.1.4 The Extension to Other Services
12.2 The Traffic Predictability in Cellular Networks
12.2.1 Prediction Dataset Description and Analysis Methodology
12.2.2 Prediction Analysis: To What Extent Is the Prior Information Required?
12.2.2.1 Temporal Dimension
12.2.2.2 Spatial Dimension
12.2.2.3 Interservice Relationship
12.3 The Prediction of Application-Level Traffic
12.3.1 Sparse Representation and Dictionary Learning
12.3.2 The Traffic Prediction Framework
12.3.2.1 Problem Formulation
12.3.2.2 Optimization Algorithm
12.3.3 Performance Evaluation
13.3 Network Softwarization View of 5G Networks
13.4 Brief History of Network Softwarization and Slicing
13.5 Issues for Slicing Towards 5G
13.5.1 Horizontal Extension of Slicing
13.5.2 Vertical Extension of Slicing: Data Plane Enhancement
13.5.3 Considerations for Applicability of Softwarization
13.5.4 End-to-End Reference Model for Scalable Operation
13.6 Information-Centric Network (ICN) Enabled by Network Softwarization
13.6.1 General Characteristics
13.6.1.2 Content Access by Its Name
13.6.1.3 Traffic Reduction by In-Network Caching
13.6.1.4 Provisioning of In-Network Data Processing
13.6.1.5 Content Security
13.6.1.6 Robustness to Network Failures by Multipath Routing
13.6.2 Applications of ICN
13.6.2.1 Networking in a Disaster Area
13.6.2.2 Advanced Metering Infrastructure (AMI) on a Smart Grid
13.6.2.3 Proactive Caching
13.6.2.4 Migration Scenario
13.6.2.5 Starting Network
13.6.2.6 Phased Deployment: Intermediate Phase
13.7 Studies in ITU-T SG13 Focus Group on IMT-2020
Chapter 14: Machine-Type Communication in the 5G Era:Massive and Ultrareliable Connectivity Forces of Evolution, Revolution, and Complementarity
14.3.1 Machines Serving Humans
14.3.2 Eyes and Hands to Control Industrial Systems: SCADA
14.3.2.1 Description of SCADA
14.3.2.2 Mobile Networks Support for SCADA
14.3.2.3 Data Processing in SCADA Systems
14.3.2.4 National Electricity Grid Example
14.3.3.1 Digital Transformation of the Machines
14.3.3.2 Cyber-Physical System Requirements
14.3.3.3 Vertical Use Case Examples
14.3.3.4 Machines and Humans
14.4 Reviewing the Standardization Path So Far
14.4.1 Overview: From 3G to 4G
14.4.1.1 From “Voice-Mainly” to “IP Focus”
14.4.1.2 Machine-Type Communication
14.4.3 5G Candidate Solution Space
14.5 Conclusion on Machine-Type 5G
Part IV: Vertical 5G Applications
Chapter 15: Social-Aware Content Delivery in Device-to-Device Underlay Networks
15.3.1 Physical Layer Model
15.3.2 Social Layer Model
15.3.2.1 Estimation of Probability Distribution
15.3.2.2 Intensity of Social Relationship
15.5 Social Network-Based Content Delivery Matching Algorithm for D2D Underlay Networks
15.5.2 Preference Establishment
15.5.3 Three-Dimensional Matching Algorithm
15.5.4 Properties of the Three-Dimensional Matching Algorithm
Chapter 16: Service-Oriented Architecture for IoT Home Area Networking in 5G
16.2 Service-Oriented Architecture
16.4 Service-Oriented Architecture for Home Area Network (SoHAN)
16.4.2 Proposed SoHAN Architecture
16.4.3 The Proposed SoHAN Middleware Framework
16.4.3.1 Sensor-Dependent Sublayer
16.4.3.2 Service-Dependent Sublayer
16.5 Performance Evaluation
Chapter 17: Provisioning Unlicensed LAA Interface for Smart Grid Applications
17.2 Smart Grid Architecture-Based 5G Communications
17.2.1 Control Center Architecture
17.2.3 Neighborhood Area Network
17.3 Bandwidth Utilization Method
17.3.1 Bandwidth Detection
17.3.2 Interference Avoidance
17.3.4 Bandwidth Utilization
17.4 System Implementation and Simulation Platform
17.4.1 Enable Career Detection for LAA Unlicensed Interface
17.4.2 System Performance and Analysis
17.5 Summary and Conclusions
Part V: R&D and 5G Standardization
Chapter 18: 5G Communication System: A Network Operator Perspective
18.2 Softwarization for the 5G Communication System
18.2.1 Network Coding as a Service
18.2.1.2 Point to Multipoint
18.2.2 The Mobile Edge Cloud
18.2.3 Distributed Edge Caching and Computing
18.2.3.1 Block Codes versus Replication
18.2.3.2 Network Coding in Distributed Storage Systems
18.2.3.3 Security Aspects: Algebraic and Light Weight
18.4 5G as Game Changer in the Value Chain
Chapter 19: Toward All-IT 5G End-to-End Infrastructure
19.1.1 Background and Purpose
19.1.2 Evolution Trend of Telco Infrastructure
19.1.2.1 Telco Infrastructure Virtualization
19.1.2.2 Open Software and Hardware
19.1.2.3 Evolution into Platform to Allow “As-a-Service”
19.1.2.4 Intelligence and Operation Efficiency
19.1.3 SK Telecom’s Perspective on NFV/SDN
19.2 Development Status and Lesson Learned
19.2.1 Radio Access Network
19.2.1.1 RAN Virtualization
19.2.1.2 Mobile Edge Service (MEC)
19.2.2.1 Virtualized EPC/IMS Commercialization
19.2.2.2 NFV MANO (Management and Orchestration) Commercialization
19.2.2.3 Service Orchestration PoC
19.2.2.4 SDN-based vEPC PoC
19.2.3.1 Unified Control Function of ROADM/OTN on Commercial Network
19.2.3.2 Common Hardware Platform-based POTN
19.2.3.3 PTN/POTN Unified Control in Multi-Vendor Environment
19.2.4.1 Integration of SDN/NFV Technology
19.2.4.2 RAN Virtualization/Disaggregation
19.2.4.3 EPC Virtualization/Disaggregation
19.2.4.4 Mobile Edge Services
19.2.5 Operational Intelligence
19.2.5.1 Intelligence for Network Big Data Collection/Analytics
19.2.5.2 Telco-defined Network Management Indicators
19.2.5.3 Big Data-based Automated Operation
19.2.5.4 Monitoring and Management of Virtual Resources
19.3 Infrastructure Evolution of SK Telecom for 5G: ATSCALE
19.3.1 Evolution Direction
19.3.2 Telco Functions on COSMOS
19.3.3 ATSCALE Architecture
19.4 Detailed Architecture and Key Enabling Technology
19.4.1 Software-defined RAN
19.4.1.1 Fronthaul Enhancement
19.4.1.2 CP/UP Separation
19.4.1.3 Open Hardware and Software
19.4.1.5 Analytics (SON) Agent
19.4.2 Virtualized Core (vCore)
19.4.2.1 Decomposed Control Plane
19.4.2.2 Simple User Plane
19.4.2.3 Centralized Service Functions (CSF)
19.4.3 Unified and Converged Transport Network (uCTN)
19.4.3.1 Transport Physical Network Functions
19.4.3.2 Virtualized Transport Network Functions
19.4.3.3 Transport Infrastructure Orchestrator
19.4.4 Unified Orchestration (Unified-O)
19.4.4.1 Standardized NFVMANO Framework
19.4.4.2 End-to-End Network Orchestration
19.4.4.3 Service Orchestration
19.4.4.4 Standard Data Model
19.4.5.1 Cognitive and Intelligent Automation
19.4.5.2 End-to-End Hybrid Resource Management
19.5.1.1 Open-Source Hardware and Software Delivers Cost Savings
19.5.1.2 Optimization Based on Analytics Delivers Operation Cost Savings
19.5.2 Platformization of Telco Infrastructure
19.5.2.1 Mobile Edge Computing as a Service (MECaaS)
19.5.2.2 Analytics as a Service (AaaS)
19.5.2.3 Policy as a Service (POaaS)
19.5.3 Operation Automation
19.5.3.1 Intelligent/Automated Network Operation
19.5.3.2 Analytics and Verification with Data Analytics Capabilities
19.5.4 Deployment of Operator-Specific Functions
19.5.5 Enhanced Service Agility
19.5.5.1 Recombinable and Reusable Software Modules with Virtualization
19.6 Summary and Conclusion
Chapter 20: Standardization: The Road to 5G
20.1 The Role of Standardization
20.2 The Main Standardization Bodies
20.3 5G Standardization Process
End User License Agreement
5G Networks
：Fundamental Requirements, Enabling Technologies, and Operations Management
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