Spectrum Sharing in Cognitive Radio Networks E-Book

Prabhat Thakur

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 594

Veröffentlichungsjahr: 2021

Beschreibung

SPECTRUM SHARING IN COGNITIVE RADIO NETWORKS Discover the latest advances in spectrum sharing in wireless networks from two internationally recognized experts in the field Spectrum Sharing in Cognitive Radio Networks: Towards Highly Connected Environments delivers an in-depth and insightful examination of hybrid spectrum access techniques with advanced frame structures designed for efficient spectrum utilization. The accomplished authors present the energy and spectrum efficient frameworks used in high-demand distributed architectures by relying on the self-scheduled medium access control (SMC-MAC) protocol in cognitive radio networks. The book begins with an exploration of the fundamentals of recent advances in spectrum sharing techniques before moving onto advanced frame structures with spectrum accessing approaches and the role of spectrum prediction and spectrum monitoring to eliminate interference. The authors also cover spectrum mobility, interference, and spectrum management for connected environments in substantial detail. Spectrum Sharing in Cognitive Radio Networks: Towards Highly Connected Environments offers readers a recent and rational theoretical mathematical model of spectrum sharing strategies that can be used for practical simulation of future generation wireless communication technologies. It also highlights ongoing trends, revealing fresh research outcomes that will be of interest to active researchers in the area. Readers will also benefit from: * An inclusive study of connected environments, 3GPP Releases, and the evolution of wireless communication generations with a discussion of advanced frame structures and access strategies in cognitive radio networks * A treatment of cognitive radio networks using spectrum prediction and monitoring techniques * An analysis of the effects of imperfect spectrum monitoring on cognitive radio networks * An exploration of spectrum mobility in cognitive radio networks using spectrum prediction and monitoring techniques * An examination of MIMO-based CR-NOMA communication systems for spectral and interference efficient designs Perfect for senior undergraduate and graduate students in Electrical and Electronics Communication Engineering programs, Spectrum Sharing in Cognitive Radio Networks: Towards Highly Connected Environments will also earn a place in the libraries of professional engineers and researchers working in the field, whether in private industry, government, or academia.

Leseprobe

Spectrum Sharing in Cognitive Radio Networks

Towards Highly Connected Environments

Prabhat Thakur

and

Ghanshyam Singh

University of JohannesburgAuckland Park, Johannesburg, South Africa

This edition first published 2021© 2021 John Wiley & Sons, Inc.

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Library of Congress Cataloging‐in‐Publication Data

Names: Thakur, Prabhat, author. | Singh, Ghanshyam, author.Title: Spectrum sharing in cognitive radio networks : towards highly connected environments / Prabhat Thakur and Ghanshyam Singh, University of Johannesburg Auckland Park, Johannesburg, South Africa.Description: First edition. | Hoboken : Wiley, 2021. | Includes bibliographical references and index.Identifiers: LCCN 2021015272 (print) | LCCN 2021015273 (ebook) | ISBN 9781119665427 (hardback) | ISBN 9781119665434 (adobe pdf) | ISBN 9781119665441 (epub)Subjects: LCSH: Radio resource management (Wireless communications) | Cognitive radio networks.Classification: LCC TK5103.4873 .T44 2021 (print) | LCC TK5103.4873 (ebook) | DDC 621.384--dc23LC record available at https://lccn.loc.gov/2021015272LC ebook record available at https://lccn.loc.gov/2021015273

Cover Design: WileyCover Image: © Pobytov/Getty Images

Preface

Technology is rapidly transforming our daily live, local community, as well as entire globe and everyone should be able to participate in this transformation. Currently, we rely on the wireless connected devices and systems to not only enable on‐demand, pervasive communications for a large proportion of the population, but also other critical application areas such as scientific and medical research, industrial control and automation, and public safety. Thus, the communication technologies are crafting new corporate social responsibility initiatives which address global problems, support innovative ideas, and enable opportunities for all in recent increasingly digital world. As the wireless communication technologies and its applications continue to flourish, the demand for precious spectrum resources, which is an essential foundation block to support the wireless communications in the globally connected world, will continue to grow. Recently, due to exponential increase in the number of wireless connected devices with other bandwidth hungry services have exploded huge communication traffic and we expect that the demand for spectrum will continue to increase as new wireless technologies and applications requirements continue to emerge in the foreseeable future which results in the spectrum scarcity. This voracious enthusiasm for additional spectrum resources cannot be met by simply allocating new spectrum. Therefore, the usable capacity of spectrum must be expanded with innovative technologies, regulatory reforms, and removal of market barriers. The cognitive radio is one of the innovative technologies which has the potential to effectively address the spectrum scarcity problem and radically change the way we utilize spectrum. Due to its potential impact, various stakeholders – including regulatory policymakers, wireless device manufacturers, telecommunication service provider, and academic researchers – have shown strong interest in it, especially with respect to research and development.

The cognitive radio technology has emerged as a prone candidate for exploiting the increasingly flexible licensing (dynamic spectrum access) of the spectrum for the wireless communication system. The regulatory bodies have come to realize that most of the time, a large portion of certain licensed frequency band remain empty/unused. Therefore, to redress this, a new regulation would allow for devices which are able to sense and adapt to their spectral environment, such as cognitive radio to become secondary user and such users are wireless devices that opportunistically employ the spectrum already licensed to the primary users. The primary users generally associate with the primary spectral licensed holder and thus have higher priority right to the spectrum. The intuitive objective behind secondary spectrum licensing is to improve the spectral efficiency of the network, whereas depending on the type of licensing without affecting higher priority users.

In the cognitive radio network, the spectrum prediction, spectrum monitoring, and medium access control protocols play an important role to exploit the spectrum opportunities, manage the interference to the primary users, and coordinate. The dynamic leasing, in which some wireless devices opportunistically employ the spectrum rather than choose for a long‐term sub‐lease. In order to exploit the spectrum, we require a device which is able to sense the communication opportunity and then take actions based on the sensed environment. The cognitive radio offers a novel way of solving spectrum underutilization problems. The emergence of Federal Communication Commission's secondary market initiative has been brought both the obvious desire for spectral efficiency as well as empirical measurements showing that most of the time certain licensed frequency remain unused. The goal of secondary market initiative is to remove unnecessary regulatory barriers to new secondary market‐oriented policies. The key points of the book that benefits the readers are as follows:

Provides decent background about the fundamentals of the spectrum sharing techniques.

Explores the advanced frame structures with spectrum accessing techniques.

Role of spectrum prediction and spectrum monitoring techniques for interference‐free spectrum sharing as well as for effective spectrum mobility is analyzed.

Due to the demand of distributed architectures in various applications, the energy and spectral efficient frameworks are presented by using the self‐scheduled medium access control (SMC‐MAC) protocol in the CRNs.

The frameworks of CR‐NOMA for further improvement in the spectral efficiency as compared to CR are also the unique contributions of this book.

The interference management schemes for the spectrum sharing are illustrated.

The exploitation of CR for the internet‐of‐vehicles, that is CR‐inspired internet‐of‐vehicles, adds as a novel contribution in the book.

Thus, it is perceived that this book enables readers about the recent advances in the field of spectrum sharing, strategies of mathematical modeling, theories of spectrum sharing in addition to the primary activities of cognitive radio network. This book puts together a rich set of research articles featuring recent advances in theory, design, and analysis of cognitive radio networks with its connected environments. The book consists of 13 chapters, which cover a wide range of topics related to the cognitive radio technology, in particular, the topics covered in this book include fundamental challenges and issues in designing cognitive radio systems, information‐theoretic analysis of cognitive radio systems, spectrum sharing, spectrum sensing and coexistence issues, adaptive physical layer protocols and link adaptation techniques for cognitive radio, different techniques for spectrum access by distributed cognitive radio, and cognitive medium access control protocols. The book is organized as follows. Chapter 1 starts with the connected environments, evolution of the wireless communication as well as technical perspectives by using the Third Generation Partnership Project (3GPP) with state‐of‐the‐art spectrum sharing/access techniques and the fundamental issues related to cognitive radio networks with its several connected parameters and the major research challenges mostly from a signal processing and communication‐theoretic perspective are well presented. The potential advantages, limiting factors, and characteristic features of the existing cognitive radio spectrum sharing domains are thoroughly discussed. The comparison of various spectrum accessing techniques such as interweave spectrum access, underlay spectrum access, overlay spectrum access, and hybrid spectrum access is presented. As the complexities of wireless technologies increase, novel multidisciplinary approaches for the spectrum sharing/management are required with inputs from the technology, economics, and regulations. To identify the available spectrum resource, decision on the optimal sensing and transmission time with proper coordination among the users for spectrum access are the important characteristics of spectrum sharing methods.

Chapter 2 describes a novel hybrid‐cum‐improved spectrum access technique to improve the throughput and data loss of the cognitive radio networks. The hybrid‐cum‐improved spectrum access technique consists of two advanced frame structures which explore the hybrid spectrum accessing strategy to utilize the channel in the presence of primary user. The closed‐form expressions of the throughput and data loss for the proposed cognitive radio networks are derived numerically and the simulation results are the witness of superior performance with reference to the throughput and data loss.

Chapter 3 explores the cognitive radio networks in the high traffic environments where the spectrum prediction plays an important role to select a particular channel for spectrum sensing from the pool of channels on the basis of idle prediction probability. The considered frame structure has spectrum prediction phase before the spectrum sensing and data transmission phase. We have exploited the concept of hybrid spectrum access strategy to improve the throughput of the considered frame structure. The probability of primary users’ emergence in the data transmission period is very significant which needs to be detected to avoid data loss and interference with primary user; however, there is no mechanism to perform this function. The spectrum monitoring technique has been used simultaneously with the data transmission period which is an advanced technique that exploits the received signal characteristics to detect the emergence of primary user. The use of spectrum monitoring improves the performance of high‐traffic cognitive radio networks in terms of data loss, power loss, and interference‐at‐primary user.

Chapter 4 discusses the potential issues concerning the random selection of spectrum sensing channel after the spectrum prediction phase in the cognitive radio networks. A novel approach of improved channel selection is proposed which relies on the probabilities of channels by which predicted idle and the closed‐form expressions for the throughput of cognitive user are derived. To achieve the prediction probabilities, the pre‐channel‐state‐information is prerequisite, which may be unavailable for particular scenarios; therefore, a modified selection method is introduced to avoid the sense and stuck problem. For additional improvement in the throughput of cognitive user, a new frame structure is introduced, in which the spectrum prediction and spectrum sensing periods are exploited for simultaneous transmission of data via the underlay spectrum access technique.

Chapter 5 introduces the concept of imperfect spectrum monitoring error and has analyzed the effect of imperfections on the data loss, power wastage, interference efficiency, and energy efficiency in the cognitive radio network. The hardware impairments and channel random nature result in the imperfections in the spectrum monitoring process which are presented through the probability of spectrum monitoring error. The imperfection in spectrum monitoring degrades the performance of cognitive radio networks when analyzed for the different scenarios of the traffic intensity and probability of spectrum monitoring error.

Chapter 6 explores the cooperation among cognitive users for the homogeneous and heterogeneous cognitive radio networks for spectrum monitoring. The Binomial and Poisson‐Binomial distribution functions are used to compute the probability of spectrum monitoring error after cooperation in the homogeneous and heterogeneous cognitive radio networks, respectively. The cooperation among cognitive users for spectrum monitoring improves the performance of cognitive radio networks in terms of the data loss, interference efficiency, and energy efficiency.

Chapter 7 presents the concept of spectrum mobility by using the spectrum prediction and spectrum monitoring techniques simultaneously, to detect the emergence of primary users. In this strategy, the decision results of the spectrum prediction and monitoring techniques are fused using AND and OR fusion rules, for the detection of emergence of primary user during the data transmission. Further, the closed‐form expressions of the resource wastage, achieved throughput, interference power at primary user, and data loss for the proposed approaches are derived. In a special case, when the prediction error is zero, the graphical characteristics of all metric values overlies the spectrum monitoring approach, which further support the proposed approach.

Chapter 8 discusses a hybrid framework in the distributed cognitive radio networks with a novel frame structure and the self‐scheduled multichannel‐medium access control protocol is developed for the proposed frame structure. The distributed network architecture is a suitable option to overcome the limitations of the centralized architecture. Each cognitive user performs all the functions of spectrum accessing, individually in the distributed network architecture; therefore, the self‐scheduled multichannel‐medium access control protocol plays a key role. The hybrid spectrum access technique is used to exploit the active channels with constrained power transmission. The proposed framework is also analyzed for the perfect and imperfect spectrum sensing scenarios. It is perceived that the proposed framework outperforms the conventional self‐scheduled multichannel‐medium access control protocols as reported in the literature with reference to the spectral efficiency/utilization as well as interference efficiency.

Chapter 9 exploits a unique and key enabler technique of future generation communication which improves spectral and energy efficiency while satisfying the constraints on users’ quality‐of‐service requirements, that is the non‐orthogonal multiple access (NOMA) technique. The potential frameworks of NOMA implementation over cognitive radio (CR) as well as the feasibility of proposed frameworks are presented as CR‐NOMA framework. Further, the differences between proposed CR‐NOMA and conventional CR frameworks are discussed and the potential issues regarding the implementation of CR‐NOMA frameworks are explored.

Chapter 10 explores a spectral‐and‐interference‐efficient framework named as MIMO‐based CR–NOMA communication system using collectively three spectral efficient techniques such as cognitive radio, NOMA, and multiple‐input‐multiple‐output (MIMO). The proposed framework is analyzed and the closed‐form expressions for throughput at each user due to the number of transmitting‐and receiving‐antennas are derived numerically for the downlink and uplink scenarios. In addition to this, the total/sum throughput for different frameworks such as CR–NOMA, CR–MIMO, and MIMO‐based CR–NOMA systems is also derived for both the downlink and uplink scenarios. In order to satisfy the interference constraints at the PU due to cell‐edge/far‐user transmission in the uplink scenario, a new metric known as interference efficiency is derived. Furthermore, the proposed frameworks are simulated for downlink and uplink scenarios and the relationship between throughput of cell‐centered/near‐user and cell‐edge/far‐user are presented. The presented results reveal that the proposed MIMO‐based CR–NOMA system outperforms the existing MIMO–NOMA, CR–NOMA, and CR–OMA systems in terms of the cognitive users individual throughput, total throughput, and interference efficiency of the system.

Chapter 11 discusses the interference management between the cognitive radio networks to enhance the spectral efficiency. The cognitive radio networks are classified as interfering and non‐interfering interference scenarios, particularly the self‐interference and user‐to‐user interference, which can be managed by frequency channel and power allocation techniques. Further, the interference cancellation techniques in the cognitive radio networks are explored and have also proposed the cross‐layer interference mitigation in the networks. Moreover, the interference avoidance techniques by advancing the different constituents of cognitive cycle that are the spectrum sensing, spectrum accessing, spectrum monitoring, and spectrum mobility are illustrated.

Chapter 12 emphasizes over the potential applications of the cognitive radio such as the internet‐of‐things and internet‐of‐vehicles or vehicular networks. It started with the digitization techniques of the fourth industrial revolution (4IR), where IoTs is a very popular technique that is subdivided into the industrial IoTs (IIoTs) and consumer (CIoTs). Further, another potential application, the connected vehicles/internet‐of‐vehicles is a prominent part whose potential constituents that are vehicle‐to‐vehicle, vehicle‐to‐infrastructure, infrastructure‐to‐vehicle, vehicle‐to‐pedestrians are illustrated. The potential simulation frameworks in order to simulate the vehicular networks are discussed and a comparison of those simulation techniques and open research challenges is illustrated.

Chapter 13 explores the radio resource management perspectives in the internet‐of‐vehicles (IoVs) networks with different wireless access technologies such as vehicular communication, cellular communication, and short‐range static communication. The vehicular communication techniques comprise the dedicated short‐range communication (DSRC), wireless access for vehicular communication (WAVE), and communication architecture and land mobile (CALM). The cellular communication techniques are 3G, 4G, and 5G, wireless access for microwave (WiMAX), and satellite communication where LTE, LTE‐Advanced, and NR are popular techniques. Further, the spectrum sharing perspectives in the IoV networks with CR frameworks are explored and the potential research challenges for particular constituents of the cognitive cycle are illustrated.

In summary, the book provides a unified view of the state‐of‐the‐art of cognitive radio wireless communications and networking technology, which should be accessible to a readership with basic knowledge about wireless communications and telecommunications networking. The readership may find the rich set of references in each of the chapters very useful. The authors have performed a good job by providing a concise summary of all the chapters at the preface of the book. I would strongly recommend the book to graduate students and researchers and engineers working or intending to work in the area of cognitive radio networks and its connected environments. Although numerous journal/conference publications, tutorials, and books on cognitive radio have been published in the last few years, the vast majority of them focus on the various physical‐layer attributes of the technology. More importantly, these technical publications discuss the cognitive radio in isolation, essentially as a stand‐alone system or network, with little regard for how it may interact with legacy wireless systems or how heterogeneous cognitive radio systems may collaborate with each other. Although this book's main theme is efficient spectrum sharing in cognitive radio networks, its specific focus areas are quite different from the existing literature. The prime intend of this book is to provide a comprehensive discussion on how cognitive radio technologies can be employed to enable spectral efficient wireless communication system. In other words, the discussions in this book revolve around how cognitive radio technologies can be used to enable various wireless networks to coexist and efficiently share spectrum. The intended readership of this book includes wireless communications industry researchers and practitioners as well as researchers in academia. The readership is assumed to have background knowledge in wireless communications and networking, although they may have no in‐depth knowledge of cognitive radio technologies. The intention of this book is to introduce communication generalists to the technical challenges of the various coexistence techniques and mechanisms as well as solution approaches which are enabled by cognitive radio networks with connected environments.

This book distinguishes itself from the existing prosperous literature of cognitive radio networks. The existing literature presents a self‐contained introduction of the emerging cognitive radio networking paradigm outlining the theoretical fundamentals and requirements for enabling such a technology. The emphasis of such books is on the theoretical design, optimization, and performance evaluation of opportunistic spectrum access in cognitive radio networks. The main challenge of existing distributed opportunistic spectrum management schemes is that they do not consider the unavoidable practical limitations of today's cognitive radio networks such as the inability to measure the interference at the primary receivers. Consequently, optimizing the constrained cognitive radio network performance based only on the local interference measurements at the cognitive radio senders does not lead to truly optimal performance due to the existence of hidden or exposed primary senders. More specifically, the existing schemes have a cognitive radio sender decide its transmission strategy based on its local interference measurement – while such decisions should have been made based on the interference measurement at the nearby primary receivers to be interfered with its transmission. However, there does not exist a practical mechanism that enables a cognitive radio to determine the interference at nearby primary receivers. Furthermore, the existing transceiver technologies and spectrum measurement techniques are incapable of accurately assessing the spectrum usage over a wide frequency range due to the limitations imposed by the transceiver hardware.

This book targets a wide range of readers including but not limited to researchers, industry experts, and senior undergraduate as well as graduate students from academia. On the one hand, the readers with theoretical interests will experience an unprecedented treatment of the conventional cognitive radio network performance optimization problem that takes into account the practical limitations of recent technologies. Further, the readers interested in real‐life distributed cognitive radio network realization will be exposed to a first‐of‐its‐kind clean‐slate implementation approach that demonstrates the significant multi‐faceted performance improvement. This book offers the reader a range of interesting topics portraying the current state‐of‐the‐art in cognitive radio technologies. In simple terms, while several existing opportunistic spectrum access approaches have been developed and theoretically optimized, they are challenged by the inherent constraints of practical implementation technologies. Analyzing these constraints and proposing an attractive and practical solution to counter these limitations are the basic aims of this book.

This book is an extension of the PhD thesis of Dr. Prabhat Thakur submitted to the Jaypee University of Information Technology, Solan, India, 2018, under the supervision of Prof. Ghanshyam Singh. The authors are indebted to numerous colleagues for the valuable suggestions during the entire period of manuscript preparation. The authors are especially thankful to the Professor B N Basu and Professor S K Kak, IIT (BHU), India, for motivation. We would also like to thank publishers at John Wiley & Sons, Inc., in particular Brett Kurzman, Victoria Bradshaw, and Sarah Lemore, for their helpful guidance and encouragement during the creation of this book. The authors would not justify their work without showing the gratitude to their family members who have always been the source of strength to tirelessly work to accomplish this assignment. We would like to acknowledge and thank several colleagues as well as Masters students and PhD scholars who have made this book possible. The first author would not justify his work without showing gratitude to his family members who have always been the source of strength for working tirelessly to accomplish the assignment. I owe my deepest gratitude toward my mother Shrimati Samundri Devi and father Shri Gian Chand Thakur for their continuous support and understanding of my goals and aspirations. I give my greatest gratitude to my parents for offering all‐around support during the period of my studies and research. Their patience and sacrifices will remain my inspiration throughout my life. I am thankful to my brother Abhishek Thakur and grandmother Shrimati Banto Devi for loving me and not complaining for their share of time. Moreover, I am thankful to all the family members and relatives for loving me and encouraging at every stage of life. I am grateful to Prof. Ghanshyam Singh, University of Johannesburg, Johannesburg, South Africa, for his valuable guidance and encouragement. His vast experience and deep understanding of the subject proved to be an immense help to me. The 2nd author, Prof. Ghanshyam Singh, is also thankful to his wife, Swati Singh; daughter, Jhanvi; and son, Shivam, for sparing their time for this work.

Special Acknowledgements

We sincerely thank the authorities of University of Johannesburg, Johannesburg, South Africa, especially, Prof. Saurabh Sinha, Deputy Vice Chancellor: Research and Internationalization and Prof. Khmaies Ouahada, Head, Department of Electrical and Electronic Engineering Sciences for their kind support to come up with this book.

University of Johannesburg

Prabhat Thakur

Ghanshyam Singh

List of Acronyms

Acronym

Meaning

3GPP

Third generation partnership project

ACS

Adaptive channel sensing

ASAF

Antenna sub array formation

AS

Antenna selection

ACI

Adjacent channel interference

ADS

Automated deriving system

AV 2.0

Automated Vehicles 2.0

AV 3.0

Automated Vehicles 3.0

AWGN

Additive white Gaussian noise

BANs

Body area networks

BS

Base station

CCH

Common control channel

CSMA

Carrier sense multiple access

CA

Collision avoidance

CSI

Channel state information

CM

Cooperative spectrum monitoring

CNR

Channel‐to‐noise ratio

CoMP

Coordinated multipoint

CIoTs

Consumer IoTs

CoIoTs

Cognitive IoTs

CALM

Communication architecture for land mobile

CR

Cognitive radio

CRN

Cognitive radio network

CC

Cooperative communication

CCC

Common control channel

CCRN

Cooperative cognitive radio network

CRAHN

Cognitive radio ad‐hoc networks

CRCN

Cognitive radio cellular networks

CWLAN

Cognitive wireless local area networks

CWMN

Cognitive wireless mesh networks

CRSN

Cognitive radio sensor networks

CCU

Centralized/controlling cognitive user

CDM

Code domain multiplexing

Conv

Conventional

Conv‐Rand‐Sel

Conventional random selection method

Conv‐Pro‐Sel

Conventional proper selection method

Conv‐1st‐F

Conventional spectrum access with first advanced frame structure

Conv‐2nd‐F

Conventional spectrum access with second improved frame structure

DFT

Discrete Fourier transform

DoS

Denial‐of‐service attack

DL

Deep learning

DVB

Digital‐video broadcasting

DSRC

Dedicated short‐range communication

DTIUM

Data transmission in underlay mode

EDGE

Enhanced Data rates for GSM Evolution

FCS

Fixed channel sensing

FMC‐MAC

Flexible multi‐channel coordination medium access control

FC

Fusion center

FBMC

Filter‐bank‐based multicarrier

GPRS

General Packet Radio Service

GFDM

Generalized frequency division multiplexing

HCRN

Homogeneous/Heterogeneous cognitive radio network

HTCRNs

High traffic cognitive radio networks

HC‐MAC

Hardware‐constrained medium access control

HMM

Hidden Markov model

HSA

Hybrid spectrum access

Hybrid‐Conv‐F

Hybrid spectrum access with conventional frame structure

Hybrid‐1st‐prop‐F

Hybrid spectrum access with 1

st

proposed frame structure

Hybrid‐2nd‐prop‐F

Hybrid spectrum access with 2

nd

proposed frame structure

HSPA

High‐Speed Packet Access

ITU

International Telecommunication Union

I4.0

Industry 4.0

IV

In‐vehicle communication

IIoTs

Industrial IoTs

ISO

International Standard Organization

LDS‐CDMA

Low‐density spreading – code division multiple access

LICSPA

Low‐interference channel status prediction algorithm

LORA

LOss differentiation rate adaptation

MLP

Multilayer perceptron

MOON

M‐out‐of‐N

MIMO

Multiple‐input‐multiple‐output

MMC

Millimeter‐wave communication

MA

Multiple access

MAC

Medium access control

M2H

Machine‐to‐human

MUSA

Multiuser shared access

MUSIC

Multiple signal classification

NCM

Non‐cooperative spectrum monitoring

NP

Neyman–Pearson

NC

Number of channels

NN

Neural network

NHTSA

National Highway Traffic Safety Administration

NTIA

National Telecommunications and Information Administration

NOMA

Non‐orthogonal multiple access

NFV

Network function virtualization

OMC‐MAC

Opportunistic multi‐channel medium access control

OFDMA

Orthogonal frequency division multiple access

OSI

Open system interconnection

PUEA

Primary user emulation attack

PO‐MAC

Pre‐emptive opportunistic medium access control

Prop‐Pro‐Sel

Proposed proper selection method

Prop‐Rand‐Sel

Proposed random selection method

PDM

Power domain multiplexing

PTDMA

Pattern division multiple access

PD‐NOMA

Power domain‐NOMA

PUDM

PU‐first‐decoding mode

SFDM

CU/SU‐first‐decoding mode

QoS

Quality‐of‐service

QoE

Quality of experience

QPSK

Quadrate phase‐shift keying

REC

Receiver error count

RSMA

Resource shared multiple access

RFID

Radio‐frequency identification

SUMO

Simulation of urban mobility

SM

Spectrum monitoring

SSA

Static spectrum access

SCMA

Sparse code multiple access

SC

Superposition coding

SIC

Successive interference cancellation

SNR

Signal‐to‐noise ratio

SJAS

Subset‐based joint AS

SINR

Signal‐to‐ interference‐plus‐noise ratio

SWIPT

Simultaneous wireless information and power transfer

SDWN

Software‐defined wireless networking

STFT

Short‐time Fourier transform

TCS

True channel states

TV

Television

TRAI

Telecommunication Regulatory Authority of India

UFMC

Universal filtered multi‐carrier

UMTS

Ultra mobile telecommunication services

V2V

Vehicle‐to‐vehicle

V2B

Vehicle‐to‐broadband

V2I

Vehicle‐to‐infrastructure

V2P

Vehicle‐to‐pedestrians

VANET

Vehicular ad‐hoc network

WRAN

Wireless regional area network

WBANs

Wireless body area networks

WSNs

Wireless sensors networks

WAVE

Wireless access for vehicular communication

WiMAX

Worldwide Interoperability for Microwave Access

Wi‐Fi

Wireless Fidelity

List of Tables

Table 1.1 List of acronyms in Figure 1.2.

Table 1.2 Spectrum sharing techniques based on the spectrum accessing approaches (SAAs).

Table 2.1 The data rates of CU for various conditions.

Table 2.2 The numerical values of the simulation metrics.

Table 3.1 The throughput of CU for different conditions.

Table 3.2 The probability distribution of true and predicted channel states.

Table 3.3 The probability distribution of true and sensing channel states.

Table 3.4 The probability distribution of the combination of true channel, prediction, and sensing states.

Table 3.5 The simulation parameters for the proposed CRN.

Table 4.1 Simulation parameters with their values.

Table 5.1 The simulation parameters for the proposed HTCRN system.

Table 6.1 The simulation parameters for the proposed HCRN.

Table 7.1 The probability distribution of true and predicted channel states.

Table 7.2 The probability distribution of true and monitoring channel states.

Table 7.3 The probability distribution of the combination of the true channel, prediction and monitoring states.

Table 7.4 The simulation metric values.

Table 8.1 The data rates of CU for various conditions.

Table 8.2 The number of channels in the CRN for various conditions.

Table 8.3 The data rates of CU for various conditions.

Table 8.4 The numerical values of simulation metrics.

Table 9.1 Comparison of various multiple accessing strategies.

Table 9.2 Cognitive radio spectrum accessing strategies.

Table 12.1 Comparison of various IoVs simulation tool.

List of Symbols

Symbol

Notation

B

D

Bandwidth of the downlink channel

B

Bandwidth of PU signal

C

(

t

)

Received signal at CU receiver due to CU transmission

DL

c

Data loss in the conventional approach

DL

1

Data loss in the first proposed approach

DL

2

Data loss in the second proposed approach

DL

Data loss

EL

Energy loss

EST

Effective switching time

f

s

Sampling frequency

IE

Interference efficiency

IF

Interference to the PU communication due to CU transmission

IP

Interference power at PU

h

ss

Channel gain coefficient between CU transmitter and CU receiver

h

ps

Channel gain coefficient from PU transmitter to CU receiver

h

sp

Channel gain from CU transmitter to PU receiver

h

cci

Channel gain coefficient between the CUs' in a base station and

i

th CU in the network

h

pci

Channel gain coefficient between the PU‐base station and

i

th CU in the network

h

Di

Channel gains from the base station to the

i

th users

H

D

i

The channel matrix from BS to the

i

th CU in the downlink scenario

H

D

P

The channel matrix from the PU transmitter to the CU‐4 in the downlink scenario

H

U

i

The channel matrix from

i

th CU to BS in the uplink scenario

H

U

P

The channel matrix from the CU‐4 to PU transmitter in the downlink scenario

H

0

Hypothesis for the absence of PU

H

1

Hypothesis for the presence of PU

IPU

CU

i_

NCM

The interference power introduced at PU due to

i

th CU in MIMO‐CR‐NOMA framework.

IEU

CU

4_

NCM

The interference efficiency of the

i

th CU in MIMO‐CR‐NOMA framework

K

Path loss exponent

k

avg

Average number of packets lost due to monitoring error

k

an

Average number of packets lost in the network without considering the effect of traffic intensity

k

TAEPU

Total number of packets to be transmitted after the emergence of PU

k

comp

Complete data loss in the proposed network

N

Number of channels

N

0

Number of packets in the data transmission period

N

CUi

Number of antennas on the

i

th CU

N

PUT

Number of antennas on the PU transmitter

n

U

BS

i

Noise power at the

i

th antenna of BS in uplink scenario

N

p

Noise power at CU receiver

N

D

i

The noise vectors at the

i

th CU in the downlink scenario

N

U

BS

The noise vectors at the BS in the uplink scenario

N

U

PU

The noise vectors at the PU in the uplink scenario

N

PPU

Noise power at PU receiver

N

PCU

Noise power at CU receiver

N

BS

Number of antennas on the BS

NC

Number of channels

N

0

i

N

0

i

is the noise power at user‐1

N

PPU

Noise power at PU receiver

N

PCU

Noise power at CU receiver

N

BS

Number of antennas on the BS

P

d

Probability of detection

P

f

Probability of false alarm

P

1

/

P

s

CU transmission power with interweave mode

P

2

CU transmission power with underlay mode

P

D

P

i

Power assigned for the

i

th antenna of PU transmitter in the downlink scenario

P

(

H

0

)

Probability of the channel being idle

P

(

H

1

)

Probability of the channel being active

P

pe

Probability of wrong prediction/probability of spectrum prediction error

Ρ

Traffic intensity of PU

P

sn

Probability of success in the

n

th event

P

m

Probability of misdetection

P

me

Probability of spectrum monitoring error

PE

Packet energy

PT

s

Starting time of packet

PT

E

Ending time of packet

PC

Power consumption

P

Pr

Powers desired for the spectrum prediction process

P

S

Powers desired for the spectrum sensing process

P

U

Maximum power that can be transmitted from the base station in order to avoid the interference with the adjacent cells

P

PU

The power transmitted by the PU base station

P

in

Probability of interference

P

i

Power transmitted by the

i

th user

PW

Power wastage

P

P

Power required by one packet for its complete process such as transmission, channel passing, reception, etc.

Q

me

Probability of error after cooperation

Q

D

i

Input covariance matrix of input vector

X

D

i

Q

U

i

Input covariance matrix of input vector

X

U

i

RA

Achieved throughput

RARDL

Ratio of achieved throughput to data loss

r

(

t

)

Received signal at CU receiver at time

t

RW

Resource wastage

R

DNi

Throughput of the

i

th NOMA user in downlink scenario

R

DOi

Throughput of the

i

th OMA user in downlink scenario

R

UNi

Throughput of the

i

th OMA user in uplink scenario

R

UOi

Throughput of the

i

th OMA user in uplink scenario

The number of nonzero singular values of the channel matrix

H

D

i

RD

CUi

_

MCN

Throughput achieved at the

i

th CU in the downlink scenario for the MIMO‐CR‐NOMA communication system

RD

CUi

_

MC

0

The throughput of

i