Computational Modeling and Simulation of Advanced Wireless Communication Systems

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
About the Editors
List of Contributors
Introduction
1 An Overview of Computational Modeling and Simulations in Wireless Communication Systems
	1.1 Introduction
		1.1.1 Key Contribution of the Chapter
		1.1.2 Organization of the Chapter
	1.2 Wireless Communication Systems
		1.2.1 Key Features and Components of Wireless Communication Systems
			1.2.1.1 Transmission Medium
			1.2.1.2 Transmitters and Receivers
			1.2.1.3 Frequency Bands
		1.2.1.4 Antennas
			1.2.1.5 Protocols and Standards
			1.2.1.6 Network Infrastructure
			1.2.1.7 Security Measures
			1.2.1.8 Applications
			1.2.1.9 Emerging Technologies
			1.2.1.10 Regulatory Framework
		1.2.2 Enabling Technologies of Computational Modeling and Simulations
			1.2.2.1 High-Performance Computing
			1.2.2.2 Parallel and Distributed Computing
			1.2.2.3 Software-Defined Networking and Network Function Virtualization
			1.2.2.4 Advanced Simulation Frameworks and Tools
			1.2.2.5 Massive Dataset Handling
			1.2.2.6 Machine Learning and Artificial Intelligence
			1.2.2.7 Visualization and Analytics
			1.2.2.8 Quantum Computing Simulation
			1.2.2.9 Augmented Reality and Virtual Reality
		1.2.3 An Overview of Computational Modeling and Simulations
			1.2.3.1 An Overview of Computational Modeling
			1.2.3.2 An Overview of Computational Simulations
	1.3 Computational Modeling and Simulations Merits in Wireless Communication Systems
		1.3.1 Complexity Management
		1.3.2 Performance Evaluation
		1.3.3 Prototyping and Design
		1.3.4 Scalability and Futureproofing
		1.3.5 Enhancing Reliability and Robustness
		1.3.6 Resource Optimization
		1.3.7 Risk Mitigation and Cost Reduction
	1.4 An Overview of Computational Modeling and Simulation Tools and Devices
		1.4.1 Simulation Software Tools for System-Level Analysis
			1.4.1.1 MATLAB and Simulink
			1.4.1.2 OPNET (Riverbed Modeler)
			1.4.1.3 Wireshark
			1.4.1.4 QualNet
			1.4.1.5 Omnet++
			1.4.1.6 Qualcomm’s Qualcomm Advanced Diagnostic Tool (QXDM) and Qualcomm EXtensible Diagnostic Monitor (QCAT)
			1.4.1.7 Network Simulator Tools (Ns-2 and Beyond)
		1.4.2 Simulation Devices and Hardware for Realistic Testing
			1.4.2.1 Software-Defined Radios
			1.4.2.2 Channel Emulators
			1.4.2.3 Multiple-Input, Multiple-Output Testbeds
			1.4.2.4 Fading Testers
			1.4.2.5 Noise Generators and Impairment Simulators
			1.4.2.6 Vehicular Emulators
		1.4.3 Device-To-Device Simulations for Emerging Technologies
			1.4.3.1 Internet of Things Simulators
			1.4.3.2 Vehicular-To-Everything (V2X) Simulations
			1.4.3.3 Mobile Edge Computing Simulations
			1.4.3.4 Drone-To-Drone (D2D) Simulations
			1.4.3.5 5G D2D Simulations
		1.4.4 Integration With Testing and Measurement Equipment
			1.4.4.1 Signal Generators and Analyzers
			1.4.4.2 Protocol Testers
			1.4.4.3 Spectrum Analyzers
			1.4.4.4 Network Analyzers
			1.4.4.5 Oscilloscopes
			1.4.4.6 Channel Emulators
	1.5 Applications of Computational Modeling and Simulations
		1.5.1 Network Planning and Optimization
		1.5.2 Wireless Protocol Development
		1.5.3 Antenna Design and Optimization
		1.5.4 Interference and Coexistence Analysis
		1.5.5 Security Analysis
		1.5.6 IoT and Sensor Networks
	1.6 Challenges and Limitations
		1.6.1 Model Validity and Accuracy
		1.6.2 Computational Resources
		1.6.3 Model Validation
		1.6.4 Complex Systems
		1.6.5 Lack of Transparency and Validation Against Real-World Complexity
		1.6.6 Uncertainty and Sensitivity
		1.6.7 Ethical and Privacy Concerns
	1.7 Future Trends and Lessons Learned
		1.7.1 Future Trends
			1.7.1.1 Sixth Generation and Beyond
			1.7.1.2 Massive MIMO and Beamforming
			1.7.1.3 IoT and Edge Computing
			1.7.1.4 Spectrum Sharing and Dynamic Spectrum Access
			1.7.1.5 Artificial Intelligence-Enhanced Simulations
			1.7.1.6 Quantum Communications
			1.7.1.7 Network Slicing and Virtualization
			1.7.1.8 Resilience and Security
			1.7.1.9 Autonomous and Connected Vehicles
		1.7.2 Lessons Learned
			1.7.2.1 Role of Simulation as a Vital Tool
			1.7.2.2 Importance of Realistic Models
			1.7.2.3 Validation Is Critical
			1.7.2.4 Adaptability to Emerging Technologies
			1.7.2.5 Optimization and Efficiency
			1.7.2.6 Spectrum Management and Interference
			1.7.2.7 Security and Resilience Testing
			1.7.2.8 Ethical Considerations
			1.7.2.9 Collaboration and Interdisciplinary Approaches
	1.8 Conclusion and Future Scope
	References
2 Historical Perspectives On Computational Modeling and Simulation in Wireless Communication Systems
	2.1 Introduction
		2.1.1 Contribution
		2.1.2 Chapter Organization
	2.2 Early Discoveries in Wireless Technology and Computation
		2.2.1 Overview of Pioneering Work in Electromagnetism
		2.2.2 Marconi’s Experiments On Radio Waves for Wireless Telegraphy
		2.2.3 Vacuum Tube and Early Electronic Amplifiers and Oscillators
		2.2.4 Foundational Mathematics Bridging Theory With Computation On Signal Analysis and Transmission
	2.3 Evolution of Analog Computation
		2.3.1 Mechanical Calculators as Earliest Modeling Tools
		2.3.2 Electronic Analog Computers for Wireless System Modeling
		2.3.3 Digital Computers Enable Software-Based Modeling
	2.4 Recent Innovations in Computational Modeling
		2.4.1 Emergence of Powerful Digital Computing Technologies Post-1970s
		2.4.2 Applications of Computational Modeling and Simulation to Mobile Wireless Networks
		2.4.3 Advanced Techniques: Machine Learning, Artificial Intelligence, and Edge Computing
			2.4.3.1 Machine Learning for Network Modeling and Optimization
			2.4.3.2 Artificial Intelligence for Network Management and Automation
			2.4.3.3 Edge Computing for Real-Time Network Analysis and Control
	2.5 Case Studies Demonstrating Evolution of Modeling in Wireless Systems
		2.5.1 Early Radio Propagation Models for Coverage Analysis
		2.5.2 Impact of Computational Modeling and Simulation Evolution in Early Wireless Communication Systems
		2.5.3 Limitations of Computational Modeling and Simulation Evolution in Early Wireless Communication System
		2.5.4 Modeling of Cellular Network Architectures and Protocols
		2.5.5 Simulation Platforms for Wireless Research
	2.6 Lessons Learned
	2.7 Technical Outlook and Emerging Trends
	2.8 Conclusion
	References
3 Computational Modelling of Communication Systems and Networks
	3.1 Introduction
		3.1.1 Motivation
		3.1.2 Contribution of the Chapter
		3.1.3 Organisation of the Chapter
	3.2 Related Work On Stochastic Geometry-Based Models in Wireless Networks
	3.3 Method and Mathematical Models: SG and Federated Learning in Wireless Networks
		3.3.1 Model Fundamentals for Wireless Networks
		3.3.2 Propagation Model
		3.3.3 Node Positioning Model
		3.3.4 Poisson Process Modelling
		3.3.5 Mathematical Models for SG and FL in Wireless Networks: A System Model
			3.3.5.2 Mathematical Formulation of Probability Coverage of Network
	3.4 Numerical Simulation Results and Discussion of Results
		3.4.1 Discussion of Numerical Results
	3.5 Lesson Learned
	3.6 Conclusion
	References
4 Computational Modeling and Analysis of Wireless Sensor Networks
	4.1 Introduction
	4.2 Modeling of Sensor Deployments
		4.2.1 Deterministic Sensor Deployments
		4.2.2 Random Sensor Deployments
	4.3 Modeling of Sensors’ Capabilities
		4.3.1 Boolean Model
		4.3.2 Elfes Modeling
		4.3.3 Multi-Level Model
	4.4 Impact of Models On the WSN Detection Probability
		4.4.1 Partial Sensing Coverage in Deterministic WSNs
		4.4.2 Partial Sensing Coverage in Random WSNs
		4.4.3 Intrusion Detection in Random WSNs
	4.5 Related Works
		4.5.1 Traditional Machine Learning-Based Intrusion Detection
		4.5.2 Deep Learning-Based Intrusion Detection
		4.5.3 Feature Selection-Based Intrusion Detection
		4.5.4 Modern Sensing Models
		4.5.5 Computational Issues Related to Antenna Arrays (5G, MmWave, and THz)
		4.5.6 Beamforming
		4.5.7 Channel Estimation
		4.5.8 Precoding
	4.6 Conclusion
	References
5 Security Measures in Computational Modeling and Simulations
	5.1 Introduction
		5.1.1 Contributions of the Chapter
		5.1.2 Chapter Organization
	5.2 Related Work
	5.3 Computational Modeling, Simulations and Their Significance
	5.4 Computational Models for Critical Decision-Making Processes
	5.5 Technology and Non-Technology-Based Security Measures
		5.5.1 Multifactor Identification, Verification, and Authentication Techniques
		5.5.2 Challenges and Considerations
		5.5.3 Data Security in Computational Modeling
	5.6 The Importance of Securing Data Used in Computational Models
		5.6.1 Explore Data Encryption, Access Controls, and Data Integrity in Simulations
		5.6.2 Threats and Vulnerabilities in Computational Modeling
		5.6.3 Common Cybersecurity Threats Targeting Computational Modeling and Simulations
		5.6.4 Potential Vulnerabilities in Computational Modeling and Simulation Systems
		5.6.5 Authentication and Authorization
		5.6.6 Relationship Between Authentication and Authorization
	5.7 The Role of Authentication and Authorization Mechanisms in Controlling Access to Computational Modeling Resources
		5.7.1 Authorization in Controlling Access to Computational Modeling Resources
		5.7.2 The Synergy Between Authentication and Authorization
		5.7.3 Multi-Factor Authentication (MFA) and Role-Based Access Control (RBAC)
		5.7.4 Role-Based Access Control (RBAC)
		5.7.5 The Synergy Between MFA and RBAC
	5.8 Network Security for Distributed Simulations
		5.8.1 Network Security Protocols and Measures to Protect Data Transmission in Distributed Simulations
		5.8.2 Secure File Transfer Protocols
	5.9 SSH File Transfer Protocol (SFTP) and Secure Copy Protocol (SCP): Secure File Transfer for Simulation Data
		5.9.1 Choosing Between SFTP and SCP for Simulation Data
	5.10 Intrusion Detection and Prevention Systems (IDS/IPS): Enhancing Network Security
		5.10.1 Intrusion Prevention System (IPS)
		5.10.2 Benefits of IDS/IPS
		5.10.3 Techniques for Securing Communication Channels and Preventing Eavesdropping
	5.11 Secure Cloud Computing for Simulations
		5.11.1 Cloud Computing for Simulations and Associated Security Considerations
		5.11.2 Security Considerations Associated With Cloud-Based Simulations
		5.11.3 Securing Data and Resources in Cloud-Based Simulations
	5.12 Cyber Threat Intelligence for Model Security
		5.12.1 The Importance of Cyber Threat Intelligence in Identifying Potential Threats to Computational Models
		5.12.2 Monitoring and Analyzing Threats in Real Time
	5.13 Proposed Cyber Attacks Model
	5.14 Secure Development Practices for Simulation Software
		5.14.1 Developing Secure Simulation Software
		5.14.2 The Importance of Secure Software Development Life Cycles (SDLC) in Simulations
		5.14.3 Data Privacy and Compliance in Simulation Software Development
		5.14.4 Data Privacy Regulations
		5.14.5 Ensuring Data Privacy and Compliance With Regulations Such as GDPR, HIPAA
	5.15 Intrusion Detection and Prevention Systems (IDPS) in Computational Modeling Environments
		5.15.1 Types of Attacks IDPS Can Mitigate and Their Configuration
		5.15.2 Incident Response and Recovery for Simulations
		5.15.3 Developing and Executing an Effective Incident Response and Recovery Strategy
	5.16 Ethical Considerations and Responsible Modeling
		5.16.1 Responsible Modeling Practices and Ethical Use of Simulation Data
	5.17 Conclusion and Future Scope
	Acknowledgments
	References
6 Computational Models for Training, Testing, and Validating Wireless Networks and Systems
	6.1 Introduction
		6.1.1 Key Contributions of the Chapter
		6.1.2 Organisation of the Chapter
	6.2 Related Works
		6.2.1 Addressing Research Gaps of Related Works
		6.2.2 Summary of Related Works
	6.3 Methodology of the Study
		6.3.1 Data Gathering
		6.3.2 Data Cleaning
		6.3.3 Data Analysis
		6.3.4 Feature Encoding
		6.3.5 Data Splitting
		6.3.6 Model Building
		6.3.7 Model Validation/Testing
	6.4 Results and Discussion
		6.4.1 Data Analysis
			6.4.1.1 Relationship Between Network Type and the Target Attribute
			6.4.1.2 Relationship Between Network Latency and the Target Attribute
		6.4.2 Prediction of Wireless Network Signal Strength
		6.4.3 Evaluation Metrics Scores
	6.5 Lessons Learned
	6.6 Conclusion
	References
7 Modeling and Simulation of Non-Lambertian Beams-Based Vehicular Visible Light Communications in 6G and Beyond
	7.1 Introduction
		7.1.1 Key Contributions of the Chapter
		7.1.2 Chapter Organization
	7.2 Lambertian Optical Beam-Based Vehicular Visible Light Communications
		7.2.1 Lambertian Optical Beam Pattern
		7.2.2 Lambertian VVLC Channel Gain
		7.2.3 Lambertian VVLC Performance Metrics
	7.3 Non-Lambertian Optical Beam-Based Vehicular Visible Light Communications
		7.3.1 Non-Lambertian Optical Beam Patterns
		7.3.2 Non-Lambertian VVLC Channel Gain
		7.3.3 Non-Lambertian VVLC Performance Metrics
	7.4 Numerical Results and Discussions
	7.5 Conclusions and Future Scope
	Acknowledgements
	References
8 Application of Computational Modeling in Electronics Devices
	8.1 Introduction
		8.1.1 Contributions of the Chapter
		8.1.2 Chapter Organization
	8.2 Advances in Object Tracking Systems, Wireless Monitoring and Wearable Electronics
		8.2.1 Electronics Sensing and Object Tracking System
		8.2.2 Modeling of Electronics Systems for Wireless Monitoring
		8.2.3 Wearable Electronics Systems Modeling and Simulation
	8.3 Related Works
	8.4 Methodology
		8.4.1 Computational Modeling of Metal Oxide Field Effect Transistors
		8.4.2 Computational Modeling of Silicon Carbide Metal Oxide Field Effect Transistors
		8.4.3 Computational Modeling of a Wearable Pulse Sensor
	8.5 Results
		8.5.1 Results of Modeling and Simulation of Level 1 MOSFET
		8.5.2 Results of Modeling and Simulation of Silicon Carbide MOSFET
		8.5.3 Simulation of Wearable Pulse Sensors
	8.6 Discussion
	8.7 Conclusion
	References
9 High-Speed Stream Ciphers for Wireless Communication Systems: Design and Simulation
	9.1 Introduction
		9.1.1 Key Contributions of the Chapter
		9.1.2 Chapter Organization
	9.2 Related Work
		9.2.1 Literature Review
		9.2.2 Summary and Gap Identification
	9.3 Fundamentals of Stream Ciphers
		9.3.1 Basic Principles of Stream Ciphers
		9.3.2 Description of Stream Cipher Functionality
		9.3.3 General Models for Stream Ciphers
			9.3.3.1 Keystream Generators
			9.3.3.2 Output Functions in Stream Ciphers
		9.3.4 Recent Advancements in Stream Ciphering for Wireless Communication Systems
			9.3.4.1 Enhanced Security Protocols in Stream Ciphering for Advanced Wireless Communication Systems
			9.3.4.2 Efficiency Improvements for High-Speed Wireless Networks
			9.3.4.3 Integration With Advanced Wireless Technologies
			9.3.4.4 Standardization and Compliance in Stream Ciphering for Advanced Wireless Communication Systems
	9.4 Methodology for Computational Modeling and Comparative Analysis of Stream Ciphers
		9.4.1 Methodology for Assessing Computational Efficiency
		9.4.2 Cryptographic Algorithms in Computational Modeling and Simulation of Advanced Wireless Communication Systems
			9.4.2.1 Mickey [27, 28]
			9.4.2.2 Decim [29]
			9.4.2.3 Strumok-256 [30, 31]
			9.4.2.4 Strumok-512 [30, 31]
			9.4.2.5 Trivium [32–34]
			9.4.2.6 Sosemanuk [35, 36]
			9.4.2.7 Snow 2.0 [9, 37, 38]
			9.4.2.8 Salsa20 [39–41]
			9.4.2.9 Rabbit [42, 43]
			9.4.2.10 HC-128 and HC-256 [44, 45]
			9.4.2.11 AES-128 and AES-256 [46, 47]
	9.5 Results of the Computational Experiment
		9.5.1 Results of the Computational Experiment On Long Stream Encryption
		9.5.2 Results of the Computational Experiment On Packet Encryption
		9.5.3 Results of IV and Key Setup Performance Testing
	9.6 Discussion On the Research Findings
		9.6.1 Computational Modeling of Cryptographic Algorithms
		9.6.2 Results of Experimental Investigations
		9.6.3 Discussion in the Context of Advanced Wireless Systems
		9.6.4 Future Scope
	9.7 Conclusion
	Acknowledgements
	References
10 Legal Frameworks Regulating Computational Models in Wireless Communication Systems
	10.1 Introduction
		10.1.1 Key Contributions
		10.1.2 Chapter Organization
	10.2 Review of Related Works
	10.3 Review Approach
	10.4 Analysis of National Laws and Regulations Concerning Computational Models
	10.5 Examination of International Agreements and Standards Governing These Models
	10.6 Ethics and Transparency of Emerging Computational Models in Wireless Communications
	10.7 Compliance and Legal Implications of New Computational Models
		10.7.1 Compliance in the Age of Computational Models
		10.7.2 Legal Implications of Non-Compliance
		10.7.3 Compliance Challenges
		10.7.4 Implications of Non-Compliance
	10.8 Conflict Resolution in Computational Models for Wireless Communication Systems
	10.9 Lessons Learned, Research Direction and Recommendations
		10.9.1 Lessons Learned
		10.9.2 Research Direction
		10.9.3 Recommendations
	10.10 Conclusion
	References
11 Government Policies and Economics of Computational Modelling in Wireless Networks
	11.1 Introduction
		11.1.1 Key Contributions of the Chapter
		11.1.2 Chapter Organisation
	11.2 Related Work
		11.2.1 Gap Analyses
	11.3 Literature Review Approach
	11.4 Government Policies in Wireless Networks
		11.4.1 Regulation and Licensing
		11.4.2 Security and Privacy
		11.4.3 Net Neutrality
		11.4.4 Government Regulations On Equal Treatment of Internet Traffic
		11.4.5 Implications of Net Neutrality On Computational Modelling in Wireless Networks
	11.5 Economics of Computational Modelling in Wireless Networks
		11.5.1 Economic Benefits of Computational Modelling in Network Planning and Optimization
		11.5.2 Allocation of Budget and Resources for Modelling and Simulation
		11.5.3 Cost Savings Through Predictive Modelling and Performance Evaluation
	11.6 Market Potentials of Computational Models for Massive Devices in Wireless Networks
	11.7 Cost-Benefit Analysis of Emerging Computational Models for Dense Wireless Networks
	11.8 Commercialization and Investment Opportunities for Computational Models in Wireless Communications
	11.9 Lesson Learned
	11.10 Recommendations
	11.11 Conclusion
	References
12 An Enhanced Lightweight Cryptographic Algorithm Towards Securing Wireless Networks and Big Data
	12.1 Introduction
		12.1.1 Key Contributions of the Chapter
		12.1.2 Chapter Organization
	12.2 The General Applications of Wireless Networks
	12.3 Overview of the Common Security and Privacy Issues in Wireless Networks
	12.4 A Case Study of Lightweight Cryptographic for a Secure Wireless Network
		12.4.1 Encryption Process
		12.4.2 Decryption Process
		12.4.3 Performance Parameters
	12.5 The Experimental Results
	12.6 Critical Lessons Learned
	12.7 Open Issues and Future Research Directions
		12.7.1 Open Issues
		12.7.2 Future Research Directions
	12.8 Conclusion
	References
13 Computational Models Enabling Smart Teaching and Learning in Wireless Communication Systems
	13.1 Introduction
		13.1.1 Key Contributions of the Chapter
		13.1.2 Chapter Organization
	13.2 The Role of Computational Models in Improving Teaching and Learning: A Data-Pedagogy Symphony
		13.2.1 Personalization at Its Finest
		13.2.2 Identifying and Addressing Learning Gaps
		13.2.3 Changing the Teacher’s Role
		13.2.4 Unlocking the Power of Data
		13.2.5 Challenges and the Road Ahead
	13.3 Fundamentals of Wireless Communication
		13.3.1 The Signal Orchestra
		13.3.2 The Electromagnetic Spectrum
		13.3.3 Modulation and Demodulation
		13.3.4 Transmitters and Receivers
		13.3.5 Noise and Interference
		13.3.6 Beyond the Fundamentals
		13.3.7 Wireless Communication in Education: Revolutionizing Learning in the Digital Age
		13.3.8 Bridging the Digital Divide: Access to the Past, Present, and Future
		13.3.9 Social and Economic Benefits Include a Global Outlook and Inclusive Learning
		13.3.10 Implications for Education: Active Learning, Personalization, and Immersive Experiences
		13.3.11 Mobile Learning, Blended Learning, and Gamification: Examples of Specific Technologies and Use Cases
		13.3.12 Cybersecurity, Teacher Training, and Ethical Considerations: Challenges and Future Directions
		13.3.13 A Symphony of Educational Opportunities for the Future
	13.4 Wireless Education Systems’ Digital Landscape: Challenges and Opportunities
		13.4.1 Challenges
		13.4.2 Opportunity
	13.5 Application Scenarios of Computational Modeling for Teaching and Learning
		13.5.1 Exploiting the Potential: Application Case Studies
		13.5.2 Types of Computational Models (E.g., Simulation, Machine Learning, Analytical)
	13.6 Simulation Models for Wireless Education Systems
		13.6.1 Uncovering the Symphony’s Opportunities: Simulation Model Approaches
		13.6.2 Case Studies Demonstrating the Use of Simulation Models in Teaching and Learning
	13.7 Machine Learning Models for Personalized Learning
		13.7.1 Understanding the Maestro: The Power of Machine Learning Models
		13.7.2 Supervised and Unsupervised Learning Approaches in Educational Contexts
		13.7.3 Unsupervised Learning Excels in Various Challenges as Listed Here
		13.7.4 The Harmonious Ensemble: Combining Methods for Educational Insights
	13.8 Computational Models Enabling Reforms in Wireless Communication System
	13.9 Lesson Learned and Future Trends
		13.9.1 Opportunities
		13.9.2 Challenges
		13.9.3 Additional Lessons
		13.9.4 Key Findings From the Chapter
		13.9.5 Future Trends
	13.10 Conclusion
	References
14 Stochastic Geometry and Federated Learning in Computational Modeling of Communication Systems
	14.1 Introduction
		14.1.1 Key Contributions
		14.1.2 Organization of the Chapter
	14.2 Related Works
		14.2.1 Stochastic Geometry
		14.2.2 Federated Learning
		14.2.3 Related Reviews
	14.3 Stochastic Geometry Modeling
		14.3.1 Signal Interference
		14.3.2 Geometric Point Processes
		14.3.3 Communication Networks
			14.3.3.1 Ad Hoc Networks
			14.3.3.2 Cognitive Radio Networks (CRN)
			14.3.3.3 Cellular Networks
	14.4 Federated Learning Modeling
		14.4.1 Enabling Technologies
		14.4.2 Aggregation Algorithm
		14.4.3 Wireless Network Applications
	14.5 Research Trends, Lessons Learned and Future Directions
		14.5.1 Research Trends
		14.5.2 Lessons Learned
		14.5.3 Future Directions
	14.6 Conclusion
	References
15 Wireless Network Encryption: Stream Ciphers, Computational Modeling, and Security Analysis
	15.1 Introduction
		15.1.1 Key Contributions of the Chapter
		15.1.2 Chapter Organization
	15.2 Related Work
	15.3 Cryptographic Security Challenges in Advanced Wireless Communication Systems
		15.3.1 The Essence of Cryptographic Security in Wireless Networks
		15.3.2 Computational Modeling: A Crucial Tool in Cryptographic Security
		15.3.3 Statistical Testing of Stream Ciphers: Ensuring Cryptographic Security in Wireless Networks
	15.4 Computational Modeling and Simulation: NIST STS and DIEHARD in Stream Cipher Testing
		15.4.1 Methodology of NIST STS for Assessing Statistical Security of Stream Ciphers
		15.4.2 The DIEHARD Statistical Test Suite for Stream Cipher Evaluation
		15.4.3 Computational Modeling and Testing of Cryptographic Ciphers in Wireless Communication Systems
	15.5 Results of the Computational Experiment
		15.5.1 Computational Modeling and Experimental Investigations Using NIST STS
		15.5.2 Computational Modeling and Experimental Investigations Using DIEHARD
	15.6 Discussion of Results
	15.7 Conclusions and Future Scope
		15.7.1 Conclusions
		15.7.2 Future Scope
	Acknowledgements
	References
16 Computational Modeling of Enhanced Spread Spectrum Codes for Asynchronous Wireless Communication
	16.1 Introduction
		16.1.1 Key Contributions of the Chapter
		16.1.2 Chapter Organization
	16.2 Related Work and Literature Gap Analysis
		16.2.1 Analysis of Related Works
		16.2.2 Literature Gap Analysis
	16.3 Materials and Methods
		16.3.1 The Concept of Direct-Sequence Spread Spectrum
		16.3.2 Formalization of the Task in Spread Spectrum Systems
		16.3.3 Requirements for Spreading Sequences in Synchronous and Asynchronous Communication Systems
		16.3.4 Theoretical Limits and Welch Bound
	16.4 Theoretical Foundations of Spreading Sequence Generation
		16.4.1 Algebraic Framework for Spreading Sequence Generation
		16.4.2 Algebraic Structure of Galois Fields in Sequence Generation
		16.4.3 Cyclical Properties of Group Codes in Sequence Generation
		16.4.4 Example: Application to BCH Codes in Finite Field
		16.4.5 The Proposed Method for Generating Discrete Spreading Sequences Based On a Section of Cyclic Orbits of a Group Code
		16.4.5 Example: Binary (7, 3, 4) Group Code and Its Discrete Signal Formation
		16.4.6 Summary and Implications of Theoretical Findings
	16.5 Computational Modeling Process for Enhanced Spread Spectrum Codes
		16.5.1 Step-By-Step Simulation Process
		16.5.2 Simulation Parameters for Computational Modeling of Spread Spectrum Codes
	16.6 Results of Computational Modeling for Spread Spectrum Code Generation
	16.7 Comparative Analysis of Spreading Sequence Correlation Characteristics and Cardinality
	16.8 Discussion and Future Scope
		16.8.1 Overview of Research Findings
		16.8.2 Future Scope
	16.9 Conclusion
	Acknowledgements
	References
Index