Radio Access Network Slicing and Virtualization for 5G Vertical Industries E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley - IEEE
- Sprache: Englisch
Learn how radio access network (RAN) slicing allows 5G networks to adapt to a wide range of environments in this masterful resource Radio Access Network Slicing and Virtualization for 5G Vertical Industriesprovides readers with a comprehensive and authoritative examination of crucial topics in the field of radio access network (RAN) slicing. Learn from renowned experts as they detail how this technology supports and applies to various industrial sectors, including manufacturing, entertainment, public safety, public transport, healthcare, financial services, automotive, and energy utilities. Radio Access Network Slicing and Virtualization for 5G Vertical Industries explains how future wireless communication systems must be built to handle high degrees of heterogeneity, including different types of applications, device classes, physical environments, mobility levels, and carrier frequencies. The authors describe how RAN slicing can be utilized to adapt 5G technologies to such wide-ranging circumstances. The book covers a wide range of topics necessary to understand RAN slicing, including: * Physical waveforms design * Multiple service signals coexistence * RAN slicing and virtualization * Applications to 5G vertical industries in a variety of environments This book is perfect for telecom engineers and industry actors who wish to identify realistic and cost-effective concepts to support specific 5G verticals. It also belongs on the bookshelves of researchers, professors, doctoral, and postgraduate students who want to identify open issues and conduct further research.
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Table of Contents
Cover
Title Page
Copyright
About the Editors
Preface
Note
List of Contributors
List of Abbreviations
Part I: Waveforms and Mixed‐Numerology
1 ICI Cancellation Techniques Based on Data Repetition for OFDM Systems
1.1 OFDM History
1.2 OFDM Principle
1.3 Carrier Frequency Offset Effect
1.4 ICI Cancellation Techniques
1.5 Experiment on Sea
1.6 Summary
References
Note
2 Filtered OFDM: An Insight into Intrinsic In‐Band Interference
2.1 Introduction
2.2 System Model for f‐OFDM SISO System
2.3 In‐Band Interference Analysis and Discussion
2.4 Numerical Results
2.5 Conclusion
2.A Appendix
2.B Appendix
2.C Appendix
References
Notes
3 Windowed OFDM for Mixed‐Numerology 5G and Beyond Systems
3.1 Introduction
3.2 W‐OFDM System Model
3.3 Inter‐numerology Interference Analysis
3.4 Numerical Results and Discussion
3.5 Conclusions
3.A Appendix
3.B Appendix
3.C Appendix
References
Note
4 Generalized Frequency Division Multiplexing: Unified Multicarrier Framework
4.1 Overview of Multicarrier Waveforms
4.2 GFDM As a Flexible Framework
4.3 GFDM for OFDM Enhancement
4.4 Conclusions
References
Notes
5 Filter Bank Multicarrier Modulation
5.1 Introduction
5.2 FBMC Methods
5.3 Theory
5.4 Prototype Filter Design
5.5 Synchronization and Tracking Methods
5.6 Equalization
5.7 Computational Complexity
5.8 Applications
References
6 Orthogonal Time–Frequency Space Modulation: Principles and Implementation
6.1 Introduction
6.2 OTFS Principles
6.3 OFDM‐Based OTFS
6.4 Channel Impact
6.5 Simplified Modem Structure
6.6 Complexity Analysis
6.7 Recent Results and Potential Research Directions
References
Part II: RAN Slicing and 5G Vertical Industries
7 Multi‐Numerology Waveform Parameter Assignment in 5G
7.1 Introduction
7.2 Waveform Parameter Options
7.3 Waveform Parameter Assignment
7.4 Conclusion
References
Note
8 Network Slicing with Spectrum Sharing
8.1 The Need for Spectrum Sharing
8.2 Historical Approaches to Spectrum Sharing
8.3 Network Slicing in the RAN
8.4 Radio Resource Allocation that Considers Spectrum Sharing
8.5 Isolation
8.6 Conclusions
Acknowledgments
References
Notes
9 Access Control and Handoff Policy Design for RAN Slicing
9.1 A Framework of User Access Control for RAN Slicing
9.2 Smart Handoff Policy Design for RAN Slicing
9.3 Summary
Bibliography
10 Robust RAN Slicing
10.1 Introduction
10.2 Network Model
10.3 Robust RAN Slicing
10.4 Numerical Results
10.5 Conclusions and Future Work
References
11 Flexible Function Split Over Ethernet Enabling RAN Slicing
11.1 Flexible Functional Split Toward RAN Slicing
11.2 Fronthaul Reliability and Slicing by Deploying Multipath at the Fronthaul
11.3 Experimentation Results Evaluation of Flexible Functional Split for RAN Slicing
11.4 Simulation Results Analysis of Multipath Packet‐Based Fronthaul for RAN Slicing
References
Note
12 Service‐Oriented RAN Support of Network Slicing
12.1 Introduction
12.2 General Concept and Principles
12.3 RAN Subsystem Deployment Scenarios
12.4 Key Technologies to Enable Service‐Oriented RAN Slicing
12.5 Summary
References
13 5G Network Slicing for V2X Communications: Technologies and Enablers
13.1 Introduction
13.2 Vehicular Applications
13.3 V2X Communication Technologies
13.4 Cloudification in V2X Environments
13.5 Transport and Tunneling Protocol for V2X
13.6 Network Slicing for V2X
13.7 Lessons Learnt and Guidelines
13.8 Conclusions
References
Notes
14 Optimizing Resource Allocation in URLLC for Real‐Time Wireless Control Systems
14.1 Introduction
14.2 System Model with Latency and Reliability Constraints
14.3 Communication‐Control Co‐Design
14.4 Optimal Resource Allocation for The Proposed Co‐Design
14.5 Simulations Results
14.6 Conclusions
References
Note
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 System parameters.
Table 1.2 Packet information.
Chapter 4
Table 4.1 GFDM‐based waveform parameters.
Table 4.2 Flexible GFDM parameters.
Chapter 6
Table 6.1 Computational complexity of different modem structures.
Chapter 7
Table 7.1 Comparison of multi‐numerology based scheduling and resource alloca...
Chapter 11
Table 11.1 Bandwidth and latency requirements for different split points.
Table 11.2 Experimentation parameters.
Table 11.3 System model parameter.
Chapter 13
Table 13.1 Connectivity requirements of V2X autonomous driving applications (...
Table 13.2 Overview of the main literature works
Chapter 14
Table 14.1 Summary of notations for communication subsystem
Table 14.2 Summary of notations for control subsystem
List of Illustrations
Chapter 1
Figure 1.1 Spectrum of OFDM signals with
, depicted by the dashed line.
Figure 1.2 The multipath and CP effects.
Figure 1.3 Block diagram of OFDM systems.
Figure 1.4 The amplitude of
.
Figure 1.5 The phase of
.
Figure 1.6 Block diagram of an OFDM transceiver with the ICI self‐cancellati...
Figure 1.7 Block diagram of an OFDM transceiver with the ICI two‐path cancel...
Figure 1.8 CIR comparison among the plain OFDM, adjacent‐mapping‐based schem...
Figure 1.9 CIR comparison among the plain OFDM, the MSR scheme, and the MCSR...
Figure 1.10 CIR comparison among the plain OFDM, the MCVT scheme, and the MC...
Figure 1.11 Geographical locations of transceiver nodes. The GPS coordinates...
Figure 1.12 BER performance comparison between OFDM‐QH and OFDM‐B for CFOs
Figure 1.13 Frame structure.
Figure 1.14 BER performance of the plain OFDM and the mirror‐mapping‐based s...
Chapter 2
Figure 2.1 Block diagram of the f‐OFDM transceiver.
Figure 2.2 f‐OFDM filtering illustration.
Figure 2.3 Power of desired signal and interference
. The three contributio...
Figure 2.4 Max, min, and average normalized power of ICI/ISI with respect to...
Figure 2.5 Average effective interference power with respect to subcarriers ...
Figure 2.6 Error performance for f‐OFDM systems under AWGN channel with QPSK...
Figure 2.7 Error performance comparison with and without implementation of B...
Chapter 3
Figure 3.1 Power spectral density comparison of the candidate 5G waveforms....
Figure 3.2 Time‐domain window processing in transmitter and receiver.
Figure 3.3 An example of mixed numerologies transmission with two subbands....
Figure 3.4 INI analysis for subband when using numerology 1.
Figure 3.5 INI analysis for numerology 1 in the CP‐OFDM case.
Figure 3.6 INI analysis for subband using numerology 2.
Figure 3.7 INI analysis for numerology 2 in CP‐OFDM case.
Figure 3.8 SIR for the adjacent subbands.
Figure 3.9 SIR comparison for W‐OFDM and OFDM systems.
Figure 3.10 SIR for different transmit window roll‐off lengths and receiver ...
Chapter 4
Figure 4.1 Subsymbol concept. In this example
,
, and
.
Figure 4.2 GFDM with multiple prototype pulses.
Figure 4.3 OQAM preprocessing.
Figure 4.4 Stages of multicarrier waveforms generator.
Figure 4.5 Stages of multicarrier waveforms receiver.
Figure 4.6 Circular convolution filter bank representation.
Figure 4.7 Zak transform.
Figure 4.8 Orthogonal GFDM. (a) Separate subsymbols and overlapped subcarrie...
Figure 4.9 GFDM modulator in four steps.
Figure 4.10 GFDM demodulator in four steps.
Figure 4.11 Unified architecture for TD and FD GFDM processing.
Figure 4.12 Precoded OFDM system model.
Chapter 5
Figure 5.1 The CMT time–frequency phase–space lattice.
Figure 5.2 Magnitude responses of the CMT pulse‐shaping filters at various s...
Figure 5.3 CMT transmitter and receiver blocks.
Figure 5.4 The SMT time–frequency phase–space lattice.
Figure 5.5 Magnitude responses of the SMT pulse‐shaping filters at various s...
Figure 5.6 SMT transmitter and receiver blocks.
Figure 5.7 Magnitude responses of a sampled and truncated SRRC filter, and t...
Figure 5.8 An FBMC data packet with short and long preambles.
Chapter 6
Figure 6.1 OTFS transmitter and receiver structure.
Figure 6.2 Simplified OFDM‐based OTFS modulator structure.
Figure 6.3 Simplified OFDM‐based OTFS demodulator structure.
Chapter 7
Figure 7.1 An example demonstration for communications with single numerolog...
Figure 7.2 Illustrations of the given subproblems and their example relation...
Figure 7.3 All of the multi‐numerology based research topics are directly as...
Figure 7.4 Waveform parameter options in 5G NR.
Figure 7.5 An example demonstration for the numerology assignment to UEs. So...
Figure 7.6 An example feature set and class labels for ML‐based joint optimi...
Chapter 8
Figure 8.1 Advantages of spectrum sharing.
Figure 8.2 Different approaches to sharing. (a) No sharing. (b) Common pool ...
Figure 8.3 Overlay spectrum sharing.
Figure 8.4 Orthogonal and non‐orthogonal sharing. (a) No spectrum sharing. (...
Figure 8.5 Creating non‐equal allocation order.
Figure 8.6 Illustration of variability in implicit sharing.
Figure 8.7 Concept of network slicing.
Figure 8.8 Types of network slicing.
Figure 8.9 Approximation to Shannon.
Figure 8.10 Comparison between fixed slices and overall sharing (even load) ...
Figure 8.11 Comparison between fixed slices and overall sharing (uneven load...
Figure 8.12 Overall results for qualified user ratio ©IEEE 2017.
Figure 8.13 Raj Jain fairness index ©IEEE 2017.
Figure 8.14 Power reduction applied as a multi‐objective optimization. (a) C...
Figure 8.15 Reduction in isolation between slices through inter‐cell interfe...
Figure 8.16 Number of qualified users as requests increase ©IEEE 2019.
Figure 8.17 Cumulative number of protected users dropped ©IEEE 2019.
Figure 8.18 Ratio of used RBs to total RBs ©IEEE 2019.
Figure 8.19 Example of dynamic change to number of PrUs ©IEEE.
Chapter 9
Figure 9.1 Network slice‐based network architecture.
Figure 9.2 Multi‐slice and multi‐AP RAN slicing.
Figure 9.3 Comparisons of the number of admissible UEs with different UE den...
Figure 9.4 Comparisons of the number of admissible UEs vs. number of NSs. (T...
Figure 9.5 Comparisons of the number of admissible UEs vs. number of BSs. (T...
Figure 9.6 NS‐based mobile network architecture.
Figure 9.7 The framework of LESS‐DL.
Figure 9.8 Comparisons of handoff performance for the four handoff mechanism...
Figure 9.9 Comparisons of handoff performance for the four handoff mechanism...
Chapter 10
Figure 10.1 The main processes of the lifecycle of a RAN slice.
Figure 10.2 Cases of substrate node and link failures. (a) source (or destin...
Figure 10.3 Failure rate of requests over time. (a) Medium uncertainty scena...
Figure 10.4 Average load of substrate links over time. (a) Medium uncertaint...
Figure 10.5 The relationship between failure rate of requests and average lo...
Figure 10.6 Average time of recovering a request over time. (a) Medium uncer...
Chapter 11
Figure 11.1 Options for functional splits for Cloud‐RAN [4].
Figure 11.2 Example of the envisioned architecture of RAN functional split s...
Figure 11.3 Setup of the testbed platform.
Figure 11.4 Latency for an IP Packet from when it is injected to PDCP layer ...
Figure 11.5 Latency from the upper to the lower layer for different splits f...
Figure 11.6 Multipath FH with erasure coding (MPC) for downlink communicatio...
Figure 11.7 Probability of error vs. latency functions for SP, MPD, and MPC ...
Figure 11.8 Probability of error vs. latency functions for SP, MPD, and MPC ...
Chapter 12
Figure 12.1 End‐to‐end network slicing.
Figure 12.2 RAN subsystem branding.
Figure 12.3 RAN subsystem deployment example on a frequency band.
Figure 12.4 RAN subsystem deployment example over multifrequency bands.
Figure 12.5 Hybrid deployment example over multifrequency bands.
Figure 12.6 Deployment scenarios of RAN parts of network slice.
Figure 12.7 Device awareness of RAN subsystem.
Figure 12.8 UE acquisition of system information.
Figure 12.9 Transitions from RRC_IDLE to RRC_CONNECTED.
Figure 12.10 RAN subsystem‐aware PDU session setup/modify.
Figure 12.11 Service‐oriented RAN topology.
Figure 12.12 Single‐connectivity with different RAN subsystems.
Figure 12.13 Dual‐connectivity with different RAN subsystems.
Figure 12.14 Slice service continuity during inter‐cell handover.
Figure 12.15 Xn based inter‐cell handover.
Figure 12.16 Illustration of UE RA with nonuniform RAN subsystems.
Chapter 13
Figure 13.1 Reference architecture for PC5 and LTE‐Uu based V2X communicatio...
Figure 13.2 5G system architecture (3GPP TS 23.501, 2019).
Figure 13.3 Uplink packet flow using
Segment Routing v6
(
SRv6
). It shows the...
Figure 13.4 Network slicing architecture and slice instantiation for autonom...
Chapter 14
Figure 14.1 Real‐time wireless control system model.
Figure 14.2 Wireless control system model for a single plant.
Figure 14.3 Average control cost as a function of time.
Figure 14.4 Optimal
with different control convergence rates
.
Figure 14.5 Optimal resource allocation with different payload information
Figure 14.6 Spectral efficiency with different payload information
.
Guide
Cover
Table of Contents
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Radio Access Network Slicing and Virtualization for 5G Vertical Industries
Edited by
Lei Zhang
University of Glasgow, Glasgow, UK
Arman Farhang
Maynooth University, Ireland
Gang Feng
University of Electronic Science and Technology of China, China
Oluwakayode Onireti
University of Glasgow, Glasgow, UK
This edition first published 2021
© 2021 John Wiley & Sons Ltd
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The right of Lei Zhang, Arman Farhang, Gang Feng, and Oluwakayode Onireti to be identified as the editors of this work has been asserted in accordance with law.
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Library of Congress Cataloging‐in‐Publication Data
Names: Zhang, Lei (Engineering teacher) editor. | Farhang, Arman, editor. |
Feng, Gang (Engineering teacher) editor. | Onireti, Oluwakayode, editor.
Title: Radio access network slicing and virtualization for 5G vertical
industries / Lei Zhang, Arman Farhang, Gang Feng, Oluwakayode Onireti.
Description: Hoboken, NJ, USA : Wiley, 2021. | Includes bibliographical
references and index.
Identifiers: LCCN 2020024172 (print) | LCCN 2020024173 (ebook) | ISBN
9781119652380 (hardback) | ISBN 9781119652458 (adobe pdf) | ISBN
9781119652472 (epub)
Subjects: LCSH: 5G mobile communication systems. | Multiple access
protocols (Computer network protocols)
Classification: LCC TK5103.25 .Z53 2021 (print) | LCC TK5103.25 (ebook) |
DDC 621.3845/6–dc23
LC record available at https://lccn.loc.gov/2020024172
LC ebook record available at https://lccn.loc.gov/2020024173
Cover Design: Wiley
Cover Image: © alexsl/Getty Images
About the Editors
Dr. Lei Zhang is a Senior Lecturer (associate professor) at the University of Glasgow, UK. He received his PhD from the University of Sheffield, UK. His research interests include wireless communication systems and networks, blockchain technology, radio access network slicing (RAN slicing), Internet of Things (IoT), multi-antenna signal processing, MIMO systems, etc. He has 19 patents granted/filed in more than 30 countries/regions including US/UK/EU/China/Japan etc. Dr. Zhang has published 2 books and 100+ peer-reviewed papers. He received IEEE Communication Society TAOS Best Paper Award 2019. He is a Technical Committee Chair of 5th International conference on UK-China Emerging Technologies (UCET) 2020. He was the Publication and Registration Chair of IEEE Sensor Array and Multichannel (SAM) 2018, Co-chair of Cyber-C Blockchain workshop 2019. He is an associate editor of IEEE Internet of Things (IoT) Journal, IEEE Wireless Communications Letters and Digital Communications and Networks. Dr. Zhang is a senior member of IEEE.
Dr. Arman Farhang received the PhD degree from the Trinity College Dublin (TCD), Dublin, Ireland, in 2016. He was a research fellow with the Irish National Telecommunications Research Centre (CONNECT), Trinity College Dublin, Dublin, Ireland, from 2016 to 2018. He was a Lecturer in the School of Electrical and Electronic Engineering at University College Dublin (UCD), Ireland, for a short period in 2018. He joined the Department of Electronic Engineering at Maynooth University in September 2018 as a Lecturer. Dr. Farhang also holds an adjunct professor position at TCD. He is an associate investigator in the CONNECT Centre, where he leads the research around the topic of waveforms for 5G and beyond. He has published over 50 peer‐reviewed publications on the topic of waveforms. He serves as an associate editor of “EURASIP Journal on Wireless Communications and Networking.” Dr. Farhang is a member of the organization committee of the IEEE Communication Society flagship conference ICC 2020. He has also served as a TPC member of many well‐reputed IEEE conferences and workshops. His research interests include wireless communications, digital signal processing for communications, multiuser communications, and multi‐antenna and multicarrier systems.
Gang Feng received the BEng and MEng degrees in electronic engineering from the University of Electronic Science and Technology of China (UESTC) in 1986 and 1989, respectively, and the PhD degree in information engineering from The Chinese University of Hong Kong in 1998. In December 2000, he joined the School of Electric and Electronic Engineering, Nanyang Technological University, Singapore, as an Assistant Professor and was promoted as an Associate Professor in October 2005. He is currently a Professor with the National Key Laboratory of Science and Technology on Communications, UESTC. He has extensive research experience and has published widely in wireless networking research. A number of his articles have been highly cited. His research interests include next generation mobile networks, mobile cloud computing, and AI‐enabled wireless networking. He has also received a number of paper awards, including the recent IEEE ComSoc TAOS Best Paper Award and the ICC Best Paper Award in 2019.
Oluwakayode Onireti received the MSc degree (distinction) in mobile and satellite communications in 2009, and the PhD degree in electronics engineering in 2012, from the Institute for Communication Systems (ICS, formally known as CCSR) of the University of Surrey. He is currently a Lecturer at the James Watt School of Engineering, University of Glasgow. He was actively involved in European Commission (EC)‐funded projects such as ROCKET and in the award‐winning EARTH project. His other project involvements include the EPSRC‐funded DARE (distributed autonomous and resilient emergency management systems) project, the QNRF‐funded QSON project, and industry‐funded projects such as the Energy Proportional EnodeB for LTE‐Advanced and Beyond project. His main research interests include self‐organizing cellular networks, energy‐efficient networks, multiple‐input multiple‐output systems, wireless blockchain networks, network slicing, and cooperative communications. He has published more than 60 technical papers in scholarly journals and international conferences. He has served as technical program committee member of several IEEE conferences and as reviewer for several IEEE and other top journals. He received an Exemplary Reviewer Award from the IEEE Transaction on Wireless Communications in 2017.
Preface
The move toward an always‐connected society, where people and machines concurrently interact with multiple devices, requires future networks to be highly flexible, combining multiple standards and architectures, while simultaneously providing services to multiple users of various types and traffic requirements (massive machine type communications (mMTC), vehicle‐to‐vehicle (V2V) communications, etc.). Moreover, future networks are expected to provide orders of magnitude improvement to such heterogeneous networks in key technical requirements such as throughput, number of connected devices, latency, and reliability.
It is cumbersome to design a unified all‐in‐one radio that meets the extreme requirements for all types of services. For instance, an MTC service might require an multicarrier system with smaller subcarrier spacing (thus longer symbol duration in the time domain) than the current standards to support massive delay‐tolerant devices, while V2V communication necessitates significantly larger frequency subcarrier spacing (thus shorter symbol duration) to satisfy the stringent delay requirements and provide robustness against Doppler spread. Moreover, some other configurations, such as waveform, should be dynamically optimized/selected to adapt to the traffic type, wireless channel, and user mobility. On the contrary, designing separate service systems that run on separate infrastructures make the operation and management of the system highly complex, expensive, and inefficient. In addition, many studies have shown that fixed spectrum allocation is wasteful since the license holders do not utilize the full spectrum continuously. Thus, to guarantee the required performance for each individual use case, the physical layer (PHY) configurations should be delicately optimized and medium access control (MAC) layer radio resources should be allocated on demand.
Radio access network (RAN) slicing is proposed to efficiently support all the aforementioned scenarios. RAN slicing enables design, deployment, customization, and optimization of isolated virtual sub‐networks, or slices on a common physical network infrastructure, as such, to accommodate all fifth generation (5G) vertical industries like massive Internet of Things (IoT) and vehicle‐to‐everything (V2X) communications. From the radio perspective, one viable RAN solution to support diverse requirements in 5G‐and‐beyond systems is to multiplex multiple types of services in one baseband system in orthogonal time and/or frequency resources, with either physical (e.g. using guard interval or guard band) or algorithmic (e.g. filtering or precoding the data) isolation to avoid/alleviate the interference between them. Frequency division multiplexing (FDM) is preferred in Third Generation Partnership Project (3GPP) for multiplexing different services due to several advantages such as forward compatibility and ease of supporting services with different latency requirements.
With such desired degree of freedom (DoF) for RAN slicing, it is expected to reap the diversity gain by optimizing air interface of individual slices. However, cohabitation of the individually optimized services, each with a different numerology Each numerology is defined by a set of parameters for the multicarrier waveform such as subcarrier‐spacing, symbol‐length, and the length of cyclic prefix., in one system leads to several technical challenges to both physical and multiple access control layers of the overall system. A fundamental challenge is how the mismatch among services with different numerologies affects signal detection, channel estimation, and equalization. In addition, the mismatch will also result in PHY configurations disorder and misalignment among services/slices. This, from the slice level viewpoint, will bring intricate interference. From the resource allocation perspective, such a high degree of heterogeneity among services fosters a complex radio system composed of three resource layers. To address the abovementioned issues, waveforms with low out‐of‐band emissions are important in multi‐service systems, as isolation between the signals corresponding to different services is required. Therefore, there is a common consensus on the need to introduce alternative technologies that complement orthogonal frequency division multiplexing (OFDM) by a more flexible and effective air interface that better serves the challenging requirements of future networks than OFDM. To this end, in the recent years, several candidate waveforms have emerged, e.g., filtered OFDM (F‐OFDM), universal filtered multicarrier (UFMC), generalized frequency division multiplexing (GFDM), filter bank multicarrier (FBMC) and orthogonal time frequency space (OTFS) modulation.
As one of the pillars that underpins multi‐service communications in future networks, the first part of this book presents the recent advances in waveform design and provides an in‐depth insight to the reader about different waveforms and their relationship with OFDM. Furthermore, the first part of the book provides a new perspective that facilitates straightforward understanding of channel equalization and the application of the new waveforms in provision of services with different requirements. It also facilitates derivation of efficient structures for synthesis and analysis of these waveforms. From the mixed numerologies coexistence perspective, the first part of the book will discuss signal detection, channel estimation, equalization, and resource allocation. These include inter‐numerology‐interference (INI) analysis of OFDM and its variants, e.g. windowed orthogonal frequency division multiplexing (W‐OFDM), F‐OFDM, new signal detection methods, effective resource allocation, and intelligent handoff algorithms while considering INI issues. The second part of the book will cover RAN slicing and virtualization and their applications to V2X communications, ultra‐reliable low‐latency communications (URLLC), etc. Additionally, the book will discuss resource sharing and optimization. Flexible function split and the design of access control and handoff policies for RAN slicing will also be discussed, followed by RAN function split, spectrum sharing, and optimizing resource allocation.
The book is suitable for telecom engineers and industry actors to aid them identify realistic and cost‐effective concepts that are uniquely tailored to support specific vertical industries. Moreover, researchers, professors, doctorate and postgraduate students would also benefit from this book, as it enables them to identify open issues and classify their research based on the existing literature. While beginners can learn about the novel techniques to characterize different modulation schemes, experienced researchers, scientists, and experts from industry can understand the extensive theoretical design fundamentals, research trends, and in particular, various perspectives on the air interface design for the future networks. Additionally, the book aims at providing in‐depth knowledge to 5G stakeholders, regulators, institutional actors, and research agencies on how 5G networks can seamlessly support vertical industries and aid them in making the right choice of the techniques/architectures while designing the next generation of wireless communication systems.
The book is split into two parts: Part I is focused on the PHY layer aspects including modulation and signaling methods, channel estimation, and mixed numerology, and Part II is focused on layers higher than PHY.
Part I
The mismatch between different services in 5G‐and‐beyond networks leads to inter‐carrier interference (ICI) and thus Chapter 1 focuses on ICI. The authors first provide an overview of OFDM, which has been deemed as the signal waveform for both uplink and downlink transmission for the 5G wireless network. The authors then discuss the impact of inter‐ICI on the carrier‐to‐interference power ratio (CIR) performance of OFDM system. The chapter then introduces four effective ICI cancellation schemes without explicitly estimating ICI coefficients based on mirror‐mapping rules and their resulting CIR performance. Finally, the chapter concludes by demonstrating the benefits of the four ICI cancellation schemes for OFDM underwater acoustic (UWA) communications that suffers from strong ICI.
Chapter 2 describes single subband matrix form for F‐OFDM where all the linear convolution operations are converted into matrix multiplications to derive a well‐channelized signal. The chapter presents the analytical derivations of the in‐band interference that is generated due to the mismatch between different services, including ICI, forward inter‐symbol interference (ISI), and backward ISI. Furthermore, the chapter presents a low‐complexity block‐wise parallel interference cancellation (BwPIC) algorithm to tackle the issue caused by the filtering operations. Finally, numerical results are included to show the effectiveness of the BwPIC algorithm.
In Chapter 3, the authors describe the INI model of W‐OFDM system in the context of mixed numerologies. The chapter establishes a theoretical model of the INI, and derives the analytical expression of its power as a function of the channel frequency response of interfering subcarriers, the spectral distance separating the aggressor and the victim subcarriers, and the overlapping windows generated by the interferer's transmitter window and the victim's receiver window. Furthermore, the chapter presents numerical results to show the signal‐to‐interference ratio (SIR) gain that can be achieved with W‐OFDM when compared with the normal OFDM.
Chapter 4 describes GFDM, which was one of the candidate waveforms for 5G, proposed as a generalized form of OFDM. The chapter highlights the role of GFDM in the implementation of multicarrier systems. Further, the chapter presents a unified time–frequency representation of GFDM, which allows a better understanding of GFDM structure and enables a comprehensive analysis. The chapter then introduces GFDM modem implementation in a practical architecture, which is more flexible than OFDM. This structure provides additional degrees of freedom for the processing of conventional and new waveforms. Finally, for practical exploitation of GFDM, the chapter introduces integration of GFDM modem as a precoding technique on top of OFDM system.
Chapter 5 presents a short review of FBMC techniques. The fundamental theory behind construction of FBMC waveforms with maximum compactness in time and frequency are presented. Moreover, a number of effective methods for designing the prototype filters for this class of multicarrier systems are reviewed. The topics of synchronization and tracking are treated in detail, and equalization and computational complexity are discussed briefly. In terms of applications, this chapter brings up a number of appealing features of FBMC waveforms that make them an ideal choice in the emerging area of massive multiple‐input multiple‐output (MIMO) networks.
Chapter 6 covers the basics of OTFS modulation and presents its discrete‐time formulation. An in‐depth analysis of the channel impact on transmit data symbols is also provided. This brings deeper insights into OTFS. The derived formulation in this chapter reveals that the precoding and post‐processing units can be combined with OFDM modulator and demodulator, respectively, which leads to a simplified modem structure. Through computational complexity analysis, this chapter shows that in realistic scenarios, OTFS modem becomes simpler than OFDM to implement.
Chapter 7 provides insights into waveform parameter assignment considering the current waveform design in 5G new radio (NR). This chapter first reviews the literature on waveform parameter assignment in the context of multi‐numerology RAN slicing. Then the waveform parameter options for the 5G NR are introduced. Finally, the chapter discusses numerology assignment, waveform processing, and the related joint optimization issues.
Part II
The authors in Chapter 8 highlight the advantages and the issues of network slicing when it is implemented in a dynamic spectrum‐sharing manner. The authors start by discussing the motivations behind spectrum sharing in future networks. This is then followed by a historical overview of the approaches to spectrum sharing. Then the concept of network slicing, different types of network slicing, and network slicing in RANs are introduced. Finally, the chapter defines the concept of isolation and presents results on isolation using connection admission control (CAC).
Chapter 9 discusses access control and handoff policy design for RAN slicing where a unified framework for access control is presented. Additionally, user access control policies to select admissible users for optimizing the quality of service (QoS) and the number of admissible users is designed. This is followed by the investigation of the handoff issue for mobile users in RAN slicing. Then a multi‐agent reinforcement learning based smart handoff policy is presented. This will reduce handoff cost while maintaining user QoS requirements in RAN slicing. Finally, the performance of the designed policy in reducing the handoff cost is numerically evaluated and compared with traditional policies without learning.
Chapter 10 presents a mechanism to address slice recovery and reconfiguration in the unified framework. This chapter first formulates a recovery problem to address the failures of substrate networks and the uncertainties of traffic demands in a RAN slice. Based on that, two robust RAN slicing algorithms are proposed for slice recovery and reconfiguration under stochastic demands. This is followed by an in‐depth assessment of the failure rate, average load of substrate attainable with the presented algorithm. Finally, a tradeoff between the robustness of slices and the average load of the links is established, which can be efficiently managed and controlled.
In Chapter 11, a flexible function split over ethernet for enabling RAN slicing in future networks is described. The chapter starts by introducing Cloud‐RAN as one of the enablers of 5G. It then introduces functional split to Cloud‐RAN with a more flexible placement of baseband functionality. This is then followed by a description of the fronthaul reliability and slicing when multipath is deployed at the fronthaul. Then the flexible functional split and the multipath packet‐based fronthaul for RAN slicing are evaluated via both experimental and numerical results.
Chapter 12 discusses service‐oriented RAN support of network slicing to meet different key performance indicators (KPIs) for different vertical services. The chapter starts by presenting the general concept and principles of service‐oriented RAN slicing. The chapter then describes the RAN system deployment scenarios, namely deployments on a single frequency band, deployment over multifrequency bands, and hybrid deployment. This chapter also introduces key technologies enabling service‐oriented RAN slicing.
In Chapter 13, 5G network slicing principles and enabling technologies for the V2X ecosystem are discussed. The chapter introduces the current and future V2X applications and the related requirements, together with the available V2X communication technologies, with a focus on the latest progress in the 3GPP and concerning radio interfaces and architecture design. The emerging segment routing technology is also investigated as a further facilitator of network slicing for V2X communications. Furthermore, the chapter summarizes the main lessons learnt from research and development activities, while also highlighting challenges and future perspectives in the promising area of network slicing for V2X communications.
Chapter 14 presents an optimal resource allocation scheme that maximizes the spectral efficiency (SE) by communication and control co‐design, where both URLLC and control convergence rate requirements are considered. The scheme enables the use of the optimal resource to support URLLC while guaranteeing the required control performance level. The chapter presents the analysis of the relationship between the control convergence rate and communication requirements and the impact of both on the URLLC quality. Finally, an iterative method to obtain the optimal resource allocation that maximizes the SE is developed. Results are included to show the effectiveness of this method.
Lei Zhang, Arman Farhang, Gang Feng, Oluwakayode Onireti
22 June 2020
Note
This work was supported in part by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant Number EP/S02476X/1, and Science Foundation Ireland (SFI) and is co-funded under the European Regional Development Fund under Grant Number 13/RC/2077.
List of Contributors
Huseyin Arslan
Electrical‐Electronics Engineering
Istanbul Medipol University
Turkey
and
Electrical Engineering
University of South Florida
USA
Claudia Campolo
Dipartimento DIIES
Universitá Mediterranea di Reggio Calabria
Italy
Marwa Chafii
ETIS UMR 8051
Université Paris Seine, Université de Cergy‐Pontoise
France
Bo Chang
National Key Laboratory of Science and Technology on Communications
University of Electronic Science and Technology of China (UESTC)
China
Xiang Cheng
School of Electronics Engineering and Computer Science
Peking University
China
Xilin Cheng
NXP Semiconductor
USA
Laurie Cuthbert
School of Applied Sciences
Macao Polytechnic Institute, Macao SAR China
Gerhard Fettweis
Vodafone Chair Mobile Communication Systems
Technische Universität Dresden
Germany
Behrouz Farhang‐Boroujeny
Electrical and Computer Engineering Department
University of Utah
USA
Arman Farhang
Department of Electronic Engineering
Maynooth University, Co.
Ireland
Gang Feng
University of Electronic Science and Technology of China
China
Feng Han
Huawei Technologies Co., LTD.
Muhammad Ali Imran
James Watt School of Engineering
University of Glasgow
UK
Yinghao Jin
Huawei Technologies Co., LTD.
Jun Li
School of Electronics and Communication Engineering
Guangzhou University
China
Liying Li
Department of Mathematics, Physics and Electrical Engineering
Northumbria University
UK
Zhongju Li
Vodafone Chair Mobile Communication Systems
Technische Universität Dresden
Germany
Yue Liu
School of Applied Sciences
Macao Polytechnic Institute, Macao SAR China
Toktam Mahmoodi
Centre for Telecommunications Research
Department of Engineering
King's College London
UK
Juquan Mao
Institute for Communication Systems (ICS)
Home of 5G Innovation Centre
University of Surrey
UK
Antonella Molinaro
Dipartimento DIIES
Universitá Mediterranea di Reggio Calabria
Feo di Vito
Italy
Ghizlane Mountaser
Centre for Telecommunications Research
Department of Engineering
King's College London
UK
Ahmad Nimr
Vodafone Chair Mobile Communication Systems
Technische Universität Dresden
Germany
Vincenzo Sciancalepore
EC Laboratories Europe GmbH
Heidelberg
Germany
Yao Sun
James Watt School of Engineering
University of Glasgow
UK
Wei Tan
Huawei Technologies Co., LTD.
Miaowen Wen
School of Electronic and Information Engineering
South China University of Technology
China
Ruihan Wen
Southwest Minzu University
China
and
University of Electronic Science and Technology of China
China
Pei Xiao
Institute for Communication Systems (ICS)
Home of 5G Innovation Centre
University of Surrey
UK
Bowen Yang
James Watt School of Engineering
University of Glasgow
UK
Chenchen Yang
Huawei Technologies Co., LTD.
Xu Yang
School of Applied Sciences
Macao Polytechnic Institute, Macao SAR China
Ahmet Yazar
Electrical‐Electronics Engineering
Istanbul Medipol University
Turkey
Lei Zhang
James Watt School of Engineering
University of Glasgow
UK
Xiaoying Zhang
Institute of Electronic Science
National University of Defense Technology
China
Guodong Zhao
James Watt School of Engineering
University of Glasgow
UK
List of Abbreviations
2D
Two‐dimensional
3GPP
Third Generation Partnership Project
4G
Fourth generation
5G
Fifth generation
5GAA
5G Automotive Association
5GC
5G core network
A/D
Analog‐to‐digital
ACI
Adjacent channel interference
ACI
Adjacent carrier interference
ACSR
Adjacent conjugate symbol repetition
AF
Application function
AMF
Access and mobility management function
AN
Access network
API
Application programming interface
AR
Augmented Reality
ARQ
Automatic repeat request
AS
Application server
ASR
Adjacent symbol repetition
ATM
Asynchronous Transfer Mode
AUSF
Authentication Server Function
AWGN
Additive white Gaussian noise
BBU
Baseband unit
BEM
Basis expansion model
BER
Bit error rate
BFGC
Block‐fading Gaussian channel
b‐ISI
Backward inter‐symbol interference
BS
Base station
BSS
Business support system
BWP
Bandwidth part
BwPIC
Block‐wise parallel interference cancellation
CA/DC
Carrier aggregation/dual connectivity
CAC
Connection admission control
CDMA
Code division multiple access
CFO
Carrier frequency offset
CFR
Channel frequency response
CIR
Carrier‐to‐interference power ratio
CMT/FBMC
Cosine modulated multitone based filter bank multicarrier
CN
Core network
CP
Cyclic prefix
CP‐OQAM
Cyclic prefix‐based offset quadrature amplitude modulation
CPRI
Common public radio interface
CR
Cognitive radio
C‐RAN
Cloud‐radio access network
CS
Cyclic suffix
CSI
Channel state information
CSMA/CA
Carrier sense multiple access/collision avoidance
CU
Central unit
C‐V2X
Cellular‐vehicle‐to‐everything
D/A
Digital‐to‐analog
DAB
Digital audio broadcasting
DC
Data center
DFT
Discrete Fourier transform
DL
Downlink
DMT
Discrete multitone
DN
Data network
DSA
Dynamic spectrum access
DSB
Double side‐band
DSCP
Differentiated Services Code Point
E2E
End‐to‐end
eMBB
Enhanced mobile broadband
eMBMS
Evolved multimedia broadcast multicast service
eNB
E‐UTRAN NodeB
EPS
Evolved Packet System
ETSI
European Telecommunications Standards Institute
FBMC
Filter bank multicarrier
FD
Frequency domain
FDE
Frequency‐domain equalization
FDCP
Frequency‐domain cyclic prefix
FDM
Frequency division multiplexing
FFT
Fast Fourier transform
f‐ISI
Forward inter‐symbol interference
FMC
Fixed mobile convergence
FMT
Filtered multitone
FMT/FBMC
Filtered multitone based filter bank multicarrier
F‐OFDM
Filtered orthogonal frequency division multiplexing
FRP
Failure recovery problem
FTD
Fractional time delay
GB
Guard band
GFDM
Generalized frequency division multiplexing
GR
Group report
GTP
GPRS Tunneling Protocol
GTP‐U
GPRS Tunneling Protocol‐user
HD
High definition
IBI
Inter‐block interference
IC
Interference cancellation
ICI
Intercarrier interference
IDFT
Inverse discrete Fourier transform
IFFT
Inverse fast Fourier transform
INI
Inter‐numerology interference
IoT
Internet of Things
IOTA
Isotropic orthogonal transform algorithm
IP
Internet protocol
ISG
Industry Specification Group
ISI
Inter‐symbol interference
ITS
Intelligent Transportation System
ITU
International Telecommunication Union
ITU‐R
ITU Radiocommunication Sector
ITU‐T
ITU Telecommunication Standardization Sector
KPI
Key performance indicator
LCM
Least common multiplier
LEO
Low Earth Orbit
LMMSE
Linear minimum mean squared error
LTE
Long‐Term Evolution
MAC
Medium access control
MANO
Management and orchestration
MCJT
Mirror conjugate transmission
MCM
Multicarrier modulation
MCMC
Markov chain Monte Carlo
MCSR
Mirror conjugate symbol repetition
MCVT
Mirror conversion transmission
MEC
Mobile edge computing
MIMO
Multiple‐input multiple‐output
mIoT
Massive Internet of Things
MIP
Mixed integer problem
ML
Machine learning
MMSE
Minimum mean squared error
mMTC
Massive machine type communications
MNO
Mobile network operator
MPLS
Multi‐Protocol Label Switching
MSR
Mirror symbol repetition
MT
Mobile terminal
MUI
Multiuser interference
MVNO
Mobile virtual network operator
NaaS
Networks as a service
NAS
Non‐access stratum
NEF
Network Exposure Function
NF
Network function
NFV
Network function virtualization
NG‐RAN
Next generation radio access network
NOA
Network operator A
NOB
Network operator B
NOMA
Non‐orthogonal multiple access
NR
New radio
NRF
Network Repository Function
NSI
Network slice instance
NSSAI
Network Slice Selection Assistance Information
NSSF
Network Slice Selection Function
O&M
Operation and maintenance
OEM
Original equipment manufacturer
Ofcom
Office of Communications
OFDM
Orthogonal frequency division multiplexing
OFDMA
Orthogonal frequency division multiple access
OOB
Out‐of‐band
OoBE
Out of band emissions
OP
Orthogonal precoding
OQAM
Offset quadrature amplitude modulation
OQAM/FBMC
Offset quadrature amplitude modulation based filter bank multicarrier
OSA
Opportunistic spectrum access
OSS
Operation support system
OTFS
Orthogonal time frequency space modulation
PA
Power amplifier
PAPR
Peak‐to‐average power ratio
PAM
Pulse amplitude modulated
PCF
Policy Control Function
PDCP
Packet data convergence protocol
PDP
Packet data protocol
PDU
Packet data unit
PHY
Physical layer
PLMN
Public land mobile network
PN
Pseudo‐random noise
PrU
Protected user
PSD
Power spectral density
PSK
Phase shift keying
PU
Primary user
QAM
Quadrature amplitude modulation
QoE
Quality of experience
QoS
Quality of service
QPSK
Quadrature phase shift keying
QU
Qualified user
QUR
Qualified user ratio
RAE
Resource allocation entity
RAN
Radio access network
RAT
Radio access technology
RB
Resource block
RC
Raised cosine
RF
Radio frequency
RLC
Radio link control
ROBUST
Robust failure recovery problem
RRC
Radio resource control
RRH
Remote radio head
RRM
Radio resource management
RSU
Road‐side unit
S/P
Serial‐to‐parallel
SC
Single carrier
SC‐FDMA
Single carrier‐frequency division multiple access
SCS
Subcarrier spacing
SD
Slice differentiator
SDN
Software‐defined networking
SDP
Semidefinite programming problem
SE
Spectral efficiency
SFFT
Symplectic finite Fourier transform
SGW
Serving gateway
SGW‐LBO
Serving gateway‐local breakout
SI
System information
SIC
Successive interference cancellation
SID
Segment identifier
SINR
Signal‐to‐interference‐plus‐noise ratio
SIR
Signal to interference ratio
SISO
Single‐input single‐output
SL
Segment list
SLA
Service level agreement
SMF
Session Management Function
SMT
Staggered multitone
SN
Substrate network
SNR
Signal‐to‐noise ratio
S‐NSSAI
Single Network Slice Selection Assistance Information
SQAM
Staggered quadrature amplitude modulation
SRH
Segment routing header
SRRC
Square‐root raised‐cosine
SRv6
Segment Routing Ipv6
SST
Slice service type
SU
Secondary user
TCP
Transmission control protocol
TD
Time domain
TDD
Time division duplexing
TDM
Time division multiplexing
TE
Traffic engineering
TEID
Tunnel endpoint identifier
TN
Transport network
T‐R
Transmitter–receiver
TTI
Transmission time interval
TV
Time varying
UDM
Unified data management
UDP
User datagram protocol
UE
User equipment
UFMC
Universal filtered multicarrier
UL
Uplink
UP
User plane
UPF
User plane function
URLLC
Ultra‐reliable low‐latency communications
UWA
Underwater acoustic
V2C
Vehicle‐to‐cloud
V2I
Vehicle‐to‐infrastructure
V2N
Vehicle‐to‐network
V2P
Vehicle‐to‐pedestrian
V2V
Vehicle‐to‐vehicle
V2X
Vehicle‐to‐everything
VDSL
Very high speed digital subscriber line
VNF
Virtual network function
VNS
Variable neighborhood search
VPN
Virtual private network
VR
Virtual reality
VRU
Vulnerable road user
VSB
Vestigial side‐band
VUE
Vehicular user equipment
WLAN
Wireless local area network
W‐OFDM
Windowed orthogonal frequency division multiplexing
ZF
Zero‐forcing
Part IWaveforms and Mixed‐Numerology
1ICI Cancellation Techniques Based on Data Repetition for OFDM Systems
Miaowen Wen1*, Jun Li2, Xilin Cheng3, and Xiang Cheng4
1School of Electronic and Information Engineering, South China University of Technology, China
2Research Center of Intelligent Communication Engineering, School of Electronics and Communication Engineering, Guangzhou University, China
3NXP Semiconductor, San Jose, CA, USA
4School of Electronics Engineering and Computer Science, Peking University, China
1.1 OFDM History
Orthogonal frequency division multiplexing (OFDM), as a special case of multicarrier transmission, is an elegant solution to the problem of high date rate transmission. OFDM was first developed to transmit data streams without inter‐symbol interference (ISI) and intercarrier interference (ICI) in the 1960s [1]. A big breakthrough for efficiently implementing the OFDM system was made in the 1970s, when discrete Fourier transform (DFT) was applied to perform baseband modulation and demodulation in OFDM [2]. Another breakthrough in OFDM is the emergence of cyclic prefix (CP) in the 1980s [3], which maintains orthogonality of the transmitted signals over multipath fading channels. In the 1990s, OFDM was exploited for wideband data communications. The first application in the commercial use of OFDM was digital audio broadcasting (DAB) in the 1980s and 1990s, where OFDM guarantees highly reliable data transmission over a high‐velocity and complex environment. At the beginning of the 2000s, wireless local area network (WLAN) applied the OFDM technique to the physical layers [4]. In recent years, OFDM has been widely used for fourth generation (4G) and fifth generation (5G) wireless systems to increase the utilization of spectrum resources as well as to combat frequency‐selective fading.
The advantages of OFDM are summarized as follows:
Resistance to frequency‐selective fading
Elimination of ISI and ICI
Efficient use of the available spectrum
Recovery of symbol lost by adequate channel coding and interleaving
Enabling one‐tap channel equalization
However, every coin has two sides. The disadvantages of OFDM lie in the following:
Large dynamic range of transmitted signal, or
peak to average power ratio
(
PAPR
)
Sensitivity to
carrier frequency offset
(
CFO
) and Doppler
1.2 OFDM Principle
Consider an OFDM system of bandwidth . The entire frequency band is divided into subbands, each of bandwidth . Given the channel coherence bandwidth satisfying , which usually holds for a sufficiently large value of , the frequency‐selective fading channel is converted into multiple‐frequency flat fading subchannels (or say subcarriers). This can be also explained in the time domain as follows. The symbol duration of the modulated signal on each subcarrier is given by . When , the symbol duration is much larger than the channel delay spread , which indicates that each subcarrier experiences flat fading. Therefore, as long as all subcarriers are orthogonal to each other, high data‐rate interference‐free parallel transmission can be achieved. How to ensure subcarrier orthogonality and perform the discrete implementation of OFDM will be discussed in detail in this section.
1.2.1 Subcarrier Orthogonality
In an OFDM system, the substream on the ‐th subcarrier is linearly modulated relative to the subcarrier frequency , and the modulated signals associated with all the subcarriers are summed together to form the transmitted signal as
where , with representing the subcarrier interval, and denoting the basic carrier frequency.
To ensure orthogonality among all subcarriers, the frequency tones must satisfy the following condition: given any two subcarriers and ,
where , . The solution to Eq. (1.2) turns out to be , where is a nonzero integer. The choice of is preferred as it leads to a most compact spectrum. A snapshot of the OFDM spectrum with is given in Figure 1.1. Figure 1.1 shows that the spectrum of one subcarrier signal overlaps that of the others. It is expected that as increases to a large value, the overall spectrum tends to be confined by a rectangle with a range of .
At the receiver, without taking into account the effects of the channel and noise, that is, the received signal is equal to the transmitted signal , the output signal on the ‐th subcarrier is given by
Figure 1.1 Spectrum of OFDM signals with , depicted by the dashed line.
Because of the orthogonality in Eq. (1.2), Eq. (1.3) can be simplified as
From the above, it is clear that the subcarrier orthogonality ensures interference‐free parallel data transmission.
Subcarrier orthogonality can be also interpreted in the frequency domain. In the frequency domain, all subcarriers should be orthogonal so as to separate out the corresponding input signal without interference. According to the Nyquist criterion, no ISI occurs when the condition that the coherence bandwidth is no less than . This implies that the minimum frequency separation required for subcarriers to remain maintain orthogonality over the symbol interval is . Obviously, the minimum frequency interval should be set to be to save the frequency resource in OFDM.
1.2.2 Discrete Implementation
According to the Nyquist criterion, to recover a signal of bandwidth without causing any distortion, the sampling frequency must be no less than . Consider the sampling time interval to be . The time‐domain discrete transmitted signal can be obtained from Eq. (1.1) as
where . Obviously, the output of Eq. (1.5) is the inverse discrete Fourier transform (IDFT) of .
Denote as the sampled time‐domain received signal. Similarly, without taking into account the channel and noise, the discrete frequency‐domain signal can be obtained from Eq. (1.3) by
which is in fact the DFT of . The subcarrier orthogonality in Eq. (1.2) also applies to the discrete‐time representation:
such that .
