Thesis Paper - Mobile& Wireless Networks – Evolution from 1980s to 5G and Beyond.

 

 

 

 

 

 

UNIVERSITA’ DEGLI STUDI GUGLIELMO MARCONI

Master of Science in Data Science & Artificial Intelligence (AI)

 

 

 

 

Mobile& Wireless Networks – Evolution from 1980s to 5G and Beyond.

 

 

Academic Advisor                                                                                        Candidate

DHANABAL T                                                                                             AZRIN ABDUL MAJID

Lithan Academy                                                                                             Rapheal Azrin

MCSLT00084

UNIVERSITA’ DEGLI STUDI GUGLIELMO MARCONI

ACADEMIC YEAR 2025

Table of Contents

Section	Page
Chapter 1 Introduction & Research Methodology	1
·         Research & Discussion on Possible Topics	5
·         Brainstorming	7
Chapter 2 - Literature Review	9
·        Thesis Preface & Introduction	11
·         Candidate Background	12
Chapter 3 - Thesis Abstract	13
·         Thesis Statement	14
·         Dedication and Thanks	15
Chapter 4 - Historical Timeline (1G to 5G and early 6G research)	22
Chapter 5 - Spectrum & Regulation (ASEAN/ANZ focus, 900 MHz, mmWave, etc.)	32
·        Annex - AI & ML for Propagator Prediction	38
Chapter 6 - Modulation, Coding, Multiple Access	39
Chapter 7 - Network Topologies & Hierarchies	44
Chapter 8 - Standards by Generation (1G–5G, WiFi, WiMAX, LoRaWAN)	51
Chapter 9- Specialized Systems & Use Cases (LoRaWAN, TETRA ATEX, ST GRID, Satellites: Beidou, Starlink, Inmarsat)	58
Chapter 10 - Core Networks (MSC to 5GC with AI/ML enhancements)	65
Chapter 11 - Security & Authentication	71
Chapter 12 - Performance, QoS, KPI Engineering (with Data Science)	76
Chapter 13 - Deployment, Optimization, Testing (AI-driven SON, ML analytics)	81
Chapter 14 - Comparative Case Studies (ASEAN & ANZ)	86
Chapter 15 - 5G Advanced & Road to 6G	91
Chapter 16 - Conclusion	96
Chapter 17 - References & Bibliography	101

 

 

Chapter 1 Introduction

 

This paper is to demonstrate the competence level of the Postgraduate Masters Degree by Coursework candidate, Mr Azrin Bin Abdul Majid aka RAPHEAL AZRIN, to the competency level for the course on Data Science and Artificial Intelligence, in the academic year of 2025.

 

The task assigned by the teaching staff is to gather information, determine a sub-deviation of the topics being tabled, and carefully expand and research into the subject matters, in a miniature brief of 12,000 worded paper , with graphics et al.

 

A Long list of topics and their Research Methodology were proposed.

 

Added to the ask is that, the usage of AI ,DATASCIENCE, Cybersecurity et al, as the candidate is attempting a Masters of Science in Data Science and AI under Lithan-UniMarconi collaboration and executive Masters program as part of the Skill future Career Transition Program, with 90% Funded by the Singapore Government vis Skillfuture Credits + CoPayment by the student on the PGDip segment.

 

The Objective of the program is to ensure the student can demonstrate the ability to apply and use case of the knowledge and skills learnt thru the SCTP Module , Data Science Modelling, Deep Learning, AML & Methods, Data Science Principles, Python for DS, DMV Data Modelling & Visualization, Generative AI & Project Management.

 

Deliberations of the modelling and reasoning brings us to the below mentioned. Following are the proposed topics tabled by the senate as appropriate for the cohort for Data Science, Security Management and Cloud Administration.

 

Generative and Agentic AI Agent services were used in the research to expedite results and research, cross referencing against the generative bias of agents such as ChatGPT5, CoPilot365, Gemini Enterprise and DeepSeek alongside other agentic workflow systems in place. CoPilot was also activated in Office365 to assist in writing and editing of the paper. All AI Systems have TRUTH MODE manually coded into the prompts, so no false information were produced and fact checked on all counts.

 

However, the candidate believes these three cohorts could be integrated into the overall master's program, allowing their skillsets to intersect and complement each other.

 

 

Research and Discussion on Possible Thesis Topics

 

Looking back at the subject matter, these are the best options proposed by the cohort and teaching staff for attempt, based on current capabilities, educational background and course outline:

• AI-Driven Cybersecurity in Mobile & Wireless Systems
  How AI/ML enhances anomaly detection, intrusion prevention, and resilience in wireless networks.
• Data Science for Distributed Databases and Secure Data Management
  Using predictive analytics, ML, and automated database management for secure, large-scale distributed data systems.
• Intelligent Error-Correcting Codes and Cryptography
  Application of AI to optimize coding algorithms (e.g., convolutional codes, Reed-Solomon) and cryptographic schemes for secure communication.

 

Brainstorming 3 possible topics

 

After much deliberation, the team members further refined and suggested the following topics for attempt:

1. AI-Powered Intrusion Detection Systems for Next-Generation Wireless Networks
2. Data Science Approaches to Secure and Efficient Distributed Database Management
3. Machine Learning Optimization of Error-Correcting Codes and Cryptographic Algorithms for Cybersecurity

 

 

Thesis Statements on the 3 possible topics

 

As candidate will be attempting a PhD Styled thesis, these thesis statements would be used to propose to the senate for the purpose of the attempted subjects.

Draft Statements for MS-PhD level Abstract and synopsis :

 

1. AI-Powered Intrusion Detection Systems for Next-Generation Wireless Networks
   This thesis investigates the integration of machine learning models into intrusion detection systems (IDS) for 5G/6G wireless infrastructures, aiming to enhance anomaly detection accuracy and reduce false positives, thereby strengthening cybersecurity in mobile environments.
2. Data Science Approaches to Secure and Efficient Distributed Database Management
   This research explores how advanced data science techniques, including predictive analytics and automated anomaly detection, can optimize performance, reliability, and security in distributed database systems handling sensitive and large-scale data.
3. Machine Learning Optimization of Error-Correcting Codes and Cryptographic Algorithms for Cybersecurity
   This thesis develops AI-enhanced optimization models for coding algorithms and cryptographic schemes, with the goal of improving error resilience, throughput, and resistance against cyber threats in high-speed network communications.

 

 

 

 

END OF CHAPTER 1

Chapter 2  Literature Review

 

Each of the proposed topics were presented with a set of Literature Review and Directional input as to the how the research would be laid out.

 

Topic 1: AI-Powered Intrusion Detection in Wireless Networks

• Foundations: Intrusion Detection Systems (IDS), anomaly vs. signature-based detection.
• Current Research: ML in IDS (e.g., Random Forest, CNN, RNN for network traffic).
• Gap: High false positive rates, adaptability in mobile/wireless contexts.
• Key Authors/Journals: IEEE Communications Surveys, ACM Computing Surveys, Elsevier Computers & Security.

Topic 2: Data Science in Distributed Databases

• Foundations: CAP theorem, distributed DB management (MongoDB, Cassandra, etc.).
• Current Research: AI for query optimization, automated fault detection.
• Gap: Balancing performance, security, and reliability in real-time.
• Key Authors/Journals: VLDB Journal, IEEE Transactions on Knowledge and Data Engineering.

Topic 3: AI-Optimized Error-Correcting Codes & Cryptography

• Foundations: Cyclic codes, convolutional codes, Reed-Solomon, block ciphers.
• Current Research: AI for decoding optimization, cryptographic key prediction prevention.
• Gap: Integrating ML with coding/crypto without introducing vulnerabilities.
• Key Authors/Journals: IEEE Transactions on Information Theory, Journal of Cryptology.

 

 

However, the preliminary subject that I will be attempting will be mobile and wireless networks system as a primary subject.

Advisor also recommended to keep the wording to 12,000 words in context at the MSc level, and expand should I dedicate to have this paper be expanded further at the PhD level later on in the year.

The take on TOPIC 1 is good, but given the density of the research, it would be good to do it at the PhD Level, once this paper has been published on Google Scholar.

 

 

Background of candidate

 

About Rapheal Azrin

 

My Profile as a Regional IM/IT Digitalization & Technology Manager at Shell Petroleum provides me a vast knowledge of know-how in the field of telecommunications and I felt I should showcase my git into this paper.

 

Candidate is also an ICANN IANA Working Stakeholder member, collaborating for RFC and IEEE technical papers.

 

Candidate also has more than 30 years of experience in the relevant field, specializing in Web Hosting, Telecommunications and Intellectual Properties.

 

Something noteworthy is that the candidate an IEEE Chartered Fellow in the field of WDRP Domain Names and Dispute Arbitration, in the aspect of copyright & Intellectual Property infringement.

 

Public information can be gathered across search engines, Crunchbase and Linkedin.

 

https://www.linkedin.com/in/azrinmajid - Linkedin Profile

 

https://icannwiki.org/Raphael_Azrin - ICANN WIKI

 

https://www.crunchbase.com/person/rapheal-azrin - Crunchbase

 

He is also an advocate for crypto & digital assets in using blockchain as an encoding technology such as Blake17,Blake19 shuffle et al.

 

 

 

END OF CHAPTER 2

 

Chapter 3 - Abstract

 

This thesis presents a comprehensive, engineering-focused analysis of mobile and wireless radio systems, charting their evolution from analog cellular networks of the 1980s through the era of 5G New Radio and into the initial phases of 6G research. Emphasis is placed on the technical lineage of air interfaces—progressing from FDMA and analog FM to TDMA, CDMA, OFDMA, and massive MIMO—and situates these innovations within the broader contexts of spectrum allocation, propagation fundamentals, and regulatory policy frameworks across ASEAN, APAC, Europe, and ANZ.

 

Topological perspectives are integrated, encompassing macro, micro, PICO, FEMTO, LPWAN, ad hoc, and satellite constellation architectures. The study details the progression of core network technologies, from circuit-switched Mobile Switching Centers (MSCs) to 5G Service-Based Architectures (SBA) and the advent of AI-native cores. Security and authentication mechanisms receive special focus, tracing the shift from legacy analog protocols to modern SIM-based authentication, mutual AKA, eSIM provisioning, SUCI identifier protection, and quantum-safe, forward-looking approaches.

 

Comparative case studies of deployments in Singapore, Malaysia, Japan, and Europe illuminate how local geography, regulation, and socio-economic conditions have shaped divergent paths of technology adoption. Special systems—including TETRA for mission-critical voice, LoRaWAN for IoT, satellite backhaul (LEO/MEO/GEO), WiMAX, Wi-Fi, Bluetooth, ZigBee, and UWB—are examined for their technical and operational trade-offs. An addendum develops the Smart Nation case study, highlighting Singapore’s PSA Tuas Mega Port, autonomous vehicles, and urban IoT ecosystems as precursors for advanced research into smart infrastructures.

 

The thesis also addresses the integration of Artificial Intelligence and Data Science, covering predictive resource allocation, anomaly detection, self-optimizing networks (SON), and machine learning-driven security as key enablers for managing next-generation system complexity. Ultimately, the work argues that the progression of mobile and wireless systems is not merely a pursuit of speed, but a convergence of communication, computation, and control, underpinning the Fourth Industrial Revolution. Concluding with a forward-looking perspective, the thesis discusses developments in 5G-Advanced and 6G research, including sub-THz spectrum, reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), and post-quantum security, positioning this work as both a technical record and a strategic roadmap for future scholarly and industrial exploration.

 

Thesis Statement

 

This thesis critically examines the technical evolution, socio-economic implications, and prospective development of mobile and wireless radio systems, tracing their advancement from first-generation (1G) analog voice networks to contemporary 5G AI-native infrastructures and emerging 6G paradigms. It posits that mobile networks have transcended their original role as communication platforms, becoming essential digital infrastructure that underpins smart nations, industrial automation, and socio-economic growth. By integrating engineering analysis, comparative regional case studies across ASEAN, APAC, Europe, and ANZ, and perspectives from Artificial Intelligence and Data Science, this research demonstrates that the design and operation of wireless systems represent a convergence of spectrum policy, architectural innovation, and intelligent automation. The thesis contends that the future of wireless connectivity will be defined by AI-enhanced, secure, and context-aware architectures, capable of supporting both mass-market services and mission-critical applications, thereby establishing 5G-Advanced and 6G as foundational enablers of the Fourth Industrial Revolution.

 

Dedication and Thanks

 

I am dedicating this thesis to my beloved daughter, IZZATI MARDIAH AZRIN whom passed away on 10^th September 2025 while I am writing this paper. Although it was a crushing blow to my emotional state of mind and to our family during this darkest moments, I strive to carry on to complete this paper , in whatever way possible for the better of others. From Allah we come, To Him we return. Nothing happens without the will of God.

 

Candidate is appreciative for the support during these times to the following persons

 

JeyaShree Rajkumar – Senior Program Manager, Lithan Academy for the inputs on the usage of Artificial Intelligence, Prompting, Programming, Workflow designs which have been heavily utilized in this program and the presentation of the thesis.

 

Andrew Toh Tze Chow – Adjuct Faculty Lecturer - Artificial Intelligence Regression and Progression Modelling techniques https://www.linkedin.com/in/andrew-toh78/

 

Fan Yeng Loon – Adjuct Faculty Lecturer – Machine Learning Technical techniques crucial insights to the technological Data Science Analytics

 

Dhanabal T – Lithan Academy / UniMarconi Adjuct Lecturer – Basis of Encyrptions in Data Communications

 

Dr Azian Razak – Zyraz Technology – CXO – ZYRAZ Global Technologies – Scaleup systems in industrialised environments optimized using Artificial Intelligence , Machine Learning and Data Science Analytics. Linkedin : https://www.linkedin.com/in/azianrazak

 

 

Dr M Rezuwan Zakaria – Sentara group – Neste Oils – Usage of Machine Learning Modelling in real time, JIT systems from Research to full scale. https://www.linkedin.com/in/rezuwanzakaria

 

Also many thanks to fellow cohort mates of PDDS0524 of the Lithan Academy:

Mahathir Humaidi - Collaboration classmate & brainstorming of the subject matters – Program Manager, Lithan Academy

https://www.linkedin.com/in/mahathir-humaid

 

Aloysius Chia -  Collaboration Classmate,AML/KYC Specialist – HSBC Singapore

https://linkedin.com/in/aloysiuschia

 

Justin Neo  - Collaboration Classmate – Business Development – HSBC Singapore

https://www.linkedin.com/in/justin-neo-01140047

 

Jimmy Seah -  Collaboration Classmate – Director – BECA

https://www.linkedin.com/in/jimmyseah/

 

Ng Yea Ling – Collaboration Classmate – PICO Creative

https://www.linkedin.com/in/yea-ling-ng-578779171/

 

Research & Scope

 

 

The evolution of mobile and wireless communication systems over the last four decades represents one of the most transformative engineering and societal achievements in human history. From the humble beginnings of analog cellular telephony in the 1980s (1G) to today’s highly sophisticated 5G networks and the research frontier of 6G, mobile and wireless technologies have fundamentally reshaped the global economy, connectivity paradigms, and the nature of human interaction. This thesis presents a comprehensive engineering-oriented overview of mobile and wireless radio systems, emphasizing their technical evolution, underlying architectures, and deployment topologies, while integrating perspectives from data science, artificial intelligence (AI), and machine learning (ML).

 

The study addresses both historical context and forward-looking perspectives, incorporating systems such as Wi-Fi, WiMAX, LoRaWAN, TETRA ATEX radios, ST GRID devices, and non-terrestrial networks including satellite constellations such as Beidou, Gouwang, Qianfan, Starlink, and Inmarsat. It also provides a comparative regional analysis focusing on ASEAN (Association of Southeast Asian Nations) and ANZ (Australia and New Zealand) markets, where diverse geographic, demographic, and regulatory conditions have shaped unique deployment trajectories.

 

The relevance of this topic is manifold. From an engineering standpoint, the radio technologies that underpin mobile networks illustrate the interplay between spectrum allocation, modulation techniques, hardware capabilities, and topological design. From a data science and AI perspective, modern networks have become too complex to manage solely through human intervention; they rely increasingly on autonomous optimization, predictive analytics, and AI-native architectures. From a socio-economic angle, mobile and wireless networks have become essential infrastructures—particularly evident during the COVID-19 pandemic when connectivity underpinned remote work, education, and healthcare.

 

Thus, this thesis aims to:

1. Trace the technical lineage of mobile and wireless systems from 1G to 5G and beyond, including parallel technologies such as Wi-Fi, WiMAX, and LPWAN.
2. Analyze the role of spectrum and regulation, with a specific emphasis on the historically significant 900 MHz band and spectrum management policies in ASEAN and ANZ.
3. Examine propagation fundamentals and how link budgets drive engineering design.
4. Investigate modulation, coding, and multiple access schemes as the building blocks of radio systems.
5. Compare network topologies and hierarchies, from macro-cells to femtocells, as well as mesh and ad hoc structures.
6. Explore specialized systems including mission-critical communications (TETRA ATEX, ST GRID) and satellite networks.
7. Assess the role of AI, ML, and Data Science in optimization, performance analysis, and next-generation innovations.
8. Provide regional case studies (ASEAN and ANZ), highlighting opportunities and challenges.
9. Conclude with a forward-looking view on 5G Advanced, 6G research themes, and the growing AI-native network paradigm.

 

Research Methodology

 

The methodology employed in this thesis adheres to a mixed qualitative–quantitative framework, designed to ensure both academic rigor and practical engineering applicability.

 

Literature Review and Bibliographic Analysis

• Primary Sources:
• 3GPP specifications (TS 36.xxx for LTE, TS 38.xxx for 5G NR).
• ITU reports (IMT-2000, IMT-Advanced, IMT-2020).
• IEEE standards (802.11 Wi-Fi, 802.16 WiMAX).
• LoRa Alliance technical documentation.
• Secondary Sources:
• Peer-reviewed academic journals (IEEE Transactions on Wireless Communications, Elsevier Computer Networks, ACM SIGCOMM).
• Industry white papers (Ericsson, Nokia, Huawei, Qualcomm, Cisco).
• Regulatory reports from ASEAN/ANZ agencies (e.g., ACMA in Australia, IMDA in Singapore, NTC in the Philippines).
• Grey Literature:
• Satellite operator documentation (Inmarsat technical manuals, Starlink deployment updates, Chinese satellite navigation whitepapers for Beidou/Gouwang/Qianfan).
• Operator field reports (Singtel, Telstra, Globe Telecom).

The literature review ensures triangulation of perspectives: academic theory, industry practice, and regulatory frameworks.

 

Comparative Case Study Method

 

The thesis uses comparative case studies for ASEAN and ANZ to illustrate how geographic, economic, and regulatory differences influence mobile system deployment.

• ASEAN: diverse spectrum of markets, from advanced (Singapore) to developing (Cambodia, Myanmar). Key themes: urban-rural divide, affordability, spectrum scarcity.
• ANZ: large land masses, low population density, leading to emphasis on satellite integration and rural coverage.

Case studies will be supported by quantitative data (coverage, latency, throughput, spectrum allocation) and qualitative analysis (policy, regulation, operator strategies).

 

 

Engineering Analysis

 

The thesis employs engineering evaluation methods, including:

• Link budget modeling: examining transmit power, antenna gains, path loss models (COST-Hata, ITU-R).
• Spectral efficiency comparisons across generations (bits/s/Hz).
• Latency and throughput trade-offs in different access schemes (TDMA vs CDMA vs OFDMA).
• QoS/KPI assessment with AI-based predictive modeling (supervised ML for anomaly detection, clustering for traffic profiling).

 

 

AI and Data Science Integration

 

Modern networks rely heavily on AI/ML techniques. This thesis incorporates:

• AI-native RAN (Radio Access Networks): Machine learning for beam management, anomaly detection, and interference prediction.
• Data Science for KPI Optimization: Predictive analytics for handover success rates, coverage holes, and traffic distribution.
• Big Data in Core Networks: Cloud-native 5G cores leveraging ML-based orchestration.
• AI-enhanced Security: ML-based intrusion detection and adaptive encryption.

 

Validation

 

To ensure validity and reliability:

• Cross-verification of technical details with multiple sources (academic + industry + regulatory).
• Quantitative benchmarks aligned with ITU/3GPP standards.
• Transparency in assumptions (e.g., propagation models assume urban macro-cell unless stated otherwise).
• Clear citation and footnoting, following UniMarconi’s guidelines.

 

Limitations

 

• Rapid pace of 5G/6G research may render certain projections outdated.
• Some proprietary satellite system details are not publicly disclosed.
• Case studies rely on available regulatory/industry data, which may not be fully standardized across regions.

• Thesis research is primarily focused on the APAC-ASEAN, specifically Singapore, Malaysia, Japan, ANZ clustering with some distinctive comparative legislations against the Euro EEC and Americas
• Machine Learning bias may be implied in the research as agentic and deep research systems were used to derive the deep dive data.

END OF CHAPTER 3

 

 

Chapter 4 - Historical timeline – 1G to 5G and Beyond

 

Introduction to the generational Evolution

 

The Evolution of Mobile and Wireless communication networks were mainly defined by the CCITT or Consultative Committee for International Telegraphs and Telephone, based out of France and later on merged into the ITU or International Telecommunications Union (www.itu.int) which is now based out of Geneva, Swiss.

The earliest way of communicating was using MORSE CODE through low band frequencies across the Atlantic thanks to WW2 where early German Enigma machine cause heavy casualties to the allied forces.

 

The evolution of mobile communication networks from the 1980s to the present has been marked by a sequence of “generational leaps” defined by standardization bodies such as the International Telecommunication Union (ITU) and the 3rd Generation Partnership Project (3GPP). Each generation (1G, 2G, 3G, 4G, and 5G) introduced new radio access technologies, modulation schemes, network architectures, and service paradigms, with 6G already emerging as a research frontier. These generational shifts were not merely technological; they were shaped by regulation, spectrum availability, socio-economic factors, and hardware capabilities.

The following sections trace this timeline in detail, highlighting technical characteristics, socio-economic impacts, and the role of AI and data science in shaping or analyzing each phase.

4.1 First Generation (1G): The Analog Era (1981–1991)

 

4.1.1 Overview

1G systems marked the beginning of cellular telephony, replacing limited-range radio phones with large-scale mobile systems. They were fully analog, primarily designed for voice services, and lacked encryption or advanced mobility features.

 

4.1.2 Key Standards

AMPS (Advanced Mobile Phone System) – deployed in North America, operating in the 800 MHz band with 30 kHz FM channels.

NMT (Nordic Mobile Telephony) – Scandinavia, 450/900 MHz bands.

TACS (Total Access Communication System) – UK, Italy, other European countries.

4.1.3 Technical Features

Multiple Access: FDMA (Frequency Division Multiple Access).

Bandwidth Efficiency: <0.5 bits/s/Hz.

Handover: Hard handoff, circuit-switched connections.

Security: Minimal; cloning and interception common.

 

4.1.4 Limitations

·         Very low capacity relative to spectrum usage.

·         No data services, SMS, or encryption.

·         Expensive handsets with limited battery life.

 

 

4.2 Second Generation (2G++): Digital Cellular (1991–2001)

 

   

 

4.2.1 Overview

2G introduced digital modulation, which enabled greater spectral efficiency, voice encryption, and SMS services. It was the first generation where mobile phones became mass-market consumer devices.

 

4.2.2 Key Standards

GSM (Global System for Mobile Communications) – TDMA-based, widely deployed in Europe, Asia, Africa. Operated primarily in 900/1800 MHz bands.

IS-95 (cdmaOne) – First commercial CDMA system, pioneered by Qualcomm, operating in the US.

IS-136 (D-AMPS) – North American digital TDMA system, less successful than GSM or CDMA.

4.2.3 Technical Features

Multiple Access: TDMA (GSM), CDMA (IS-95).

Encryption: A5/1 cipher in GSM, improved security vs. 1G.

Data Services: GPRS (General Packet Radio Service) → “2.5G”, EDGE (Enhanced Data for GSM Evolution) → “2.75G”.

Peak Speeds: 50–200 kbps.

 

4.2.4 Socio-Economic Impact

 

SMS revolutionized communication; low-cost and widely adopted.

Mobile penetration grew rapidly in ASEAN (e.g., Philippines’ “texting capital” phenomenon).

In ANZ, 2G enabled widespread rural voice coverage.

4.2.5 GPRS & EDGE

Typically known as 2.5G and 2.75G, both Global Packet Radio Service and Enhanced Data for GSM Evolution plays a pivotal role in the next step of evolution into 3G networks. It also enables Laptops (aka Mobile Computers) to be really mobile so that it can be connected to the internet. Usage includes wireless Credit Card EFTPOS devices and Taxi Booking MDT. (shown: MDT POS system for Comfort Taxi in Singapore, circa 2000s)

 

 

4.3 Third Generation (3G): Multimedia & Internet (2001–2010)

 

4.3.1 Overview

3G was standardized under ITU’s IMT-2000 umbrella, promising global roaming and broadband-like services.

 

4.3.2 Key Standards

UMTS/W-CDMA – Europe and Asia, 5 MHz carriers.

CDMA2000 (1xRTT, EV-DO) – North America and parts of Asia-Pacific.

4.3.3 Technical Features

Multiple Access: Wideband CDMA (UMTS), CDMA2000.

Peak Speeds:

o    UMTS ~384 kbps,

o    HSPA (High Speed Packet Access) up to 14 Mbps (later HSPA+).

Core Network: Introduction of packet-switched cores alongside circuit-switched.

4.3.4 Limitations

High cost of licenses in some countries (e.g., Europe).

Spectrum fragmentation slowed adoption in ASEAN.

ANZ markets adopted 3G early (Telstra’s Next G network).

Different Networks not cross compatible between US and Rest of World.

 

4.4 Fourth Generation (4G): The IP Broadband Era (2009–2019)

 

4.4.1 Overview

4G represented a paradigm shift: all-IP networks, high-speed broadband, and LTE as the global standard.

 

4.4.2 Key Standards

LTE (Long Term Evolution) – OFDMA downlink, SC-FDMA uplink.

WiMAX (802.16e/m) – Considered 4G by ITU, but commercially outcompeted by LTE.

4.4.3 Technical Features

Multiple Access: OFDMA (downlink), SC-FDMA (uplink).

Bandwidth: Up to 20 MHz per carrier; carrier aggregation up to 100 MHz.

Peak Speeds: 100 Mbps (Release 8 LTE), later >1 Gbps with LTE-Advanced.

Core Network: Evolved Packet Core (EPC), all-IP, IMS/VoLTE for voice.

4.4.4 Impact

Enabled mobile broadband, video streaming, app economy.

ASEAN: accelerated smartphone adoption, mobile financial inclusion.

ANZ: Telstra and Optus pioneered early LTE rollouts for rural broadband.

 

 

4.5 Fifth Generation (5G): Ultra-Flexible Architecture (2019–Present)

 

4.5.1 Overview

5G is not just a faster network but a flexible, software-defined platform supporting enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC).

 

4.5.2 Key Standards

3GPP Release 15 (2019) – 5G NR, NSA/SA modes.

Release 16–17 – URLLC, industrial IoT, non-terrestrial networks (NTN).

Release 18 (5G Advanced) – AI-native RAN, enhanced XR, positioning.

 

4.5.3 Technical Features

Multiple Access: Flexible OFDM numerologies (15–240 kHz spacing).

Spectrum:

o    FR1 (sub-6 GHz, e.g., 3.5 GHz).

o    FR2 (mmWave, 24–40+ GHz).

Peak Speeds: 10 Gbps in lab trials.

Latency: <1 ms target for URLLC.

MIMO: Massive MIMO (64T64R, >256 antennas), beamforming.

4.5.4 Use Cases

Smart cities, autonomous vehicles, industrial automation.

ASEAN: Singapore leads with 5G SA, others catching up.

ANZ: Focus on industrial IoT and rural coverage with low-band 5G.

 

 

4.6  Towards Sixth Generation (6G): The Research Frontier (2030+)

 

4.6.1 Emerging Concepts

Sub-THz spectrum (100 GHz–1 THz) for Tbps data rates.( Terabits)

Joint Communication and Sensing (JCAS).

Reconfigurable Intelligent Surfaces (RIS).

Integrated terrestrial–non-terrestrial networks (NTN).

AI-native core and RAN.

4.6.2 Global Research Initiatives

Europe: Hexa-X program.

China: 6G test satellite launches.

Japan/Korea: Government-industry consortia for 6G by 2030.

ASEAN/ANZ: Early research but more focused on 5G rollout.

 

 

4.7 Summary

From analog voice (1G) to AI-native, cloud-driven architectures (5G and beyond), the mobile communications timeline reflects both technical ingenuity and socio-economic drivers. The integration of AI and Data Science is not a late addition but a necessary response to the complexity of modern networks, paving the way for 6G as a cognitive, sensing-aware infrastructure.

 

END OF CHAPTER 4

 

 

Chapter 5 - Spectrum and Regulation (ASEAN/APAC Focus, 900 MHz and Beyond)

 

5.1 Introduction to Spectrum as a Resource

 

Spectrum is the most fundamental and limited natural resource in wireless communications. It is not consumable like fuel, but it is scarce and subject to strict allocation, regulation, and reuse strategies. Unlike other resources, spectrum cannot be manufactured; it must be efficiently shared and reused using frequency planning, modulation efficiency, and multi-access schemes.

The management of spectrum has been overseen globally by the International Telecommunication Union (ITU), while national regulators such as ACMA (Australian Communications and Media Authority), IMDA (Singapore), NBTC (Thailand), and others in the ASEAN region enforce localized frameworks. Spectrum licensing, auctions, and refarming are key mechanisms through which governments allocate frequencies to operators.

 

5.2 The Importance of the 900 MHz Band

 

The 900 MHz band has historically played a central role in mobile communications:

• 1G: Many early analog systems operated at 900 MHz.
• 2G GSM: 900 MHz was the global anchor band, offering balance between coverage and capacity.
• 3G UMTS: Some refarming allowed deployment of 3G in 900 MHz.
• 4G LTE: Operators in ASEAN/ANZ reused 900 MHz for LTE coverage in rural areas.
• 5G NR: While primarily sub-6 GHz and mmWave, some early deployments use refarmed 900 MHz for coverage-layer NR.

 

5.3 Propagation Benefits

• Lower path loss vs. higher bands.
• Deeper indoor penetration.
• Larger cell radius → fewer base stations needed, lowering CAPEX.

5.4 Trade-offs

• Narrower bandwidth compared to higher frequencies (limiting capacity).
• Heavily congested in many regions due to legacy users.

 

 

 

5.5 Spectrum Allocation by Generation

 

Generation	Typical Bands	Bandwidth Per Carrier	Notes
1G	800–900 MHz	25–30 kHz	Analog FDMA channels
2G (GSM)	900, 1800, 1900 MHz	200 kHz	900 MHz dominant in ASEAN/ANZ
3G (UMTS/ W-CDMA)	2.1 GHz (primary), 900 MHz (refarmed)	5 MHz	Used dual bands
4G (LTE)	700, 800, 900, 1800, 2600, 3500 MHz	1.4–20 MHz (CA up to 100 MHz)	ASEAN/ANZ diversified
5G (NR)	FR1 (600–4200 MHz), FR2 (24–40 GHz)	5–100 MHz (FR1), 100–400 MHz (FR2)	Auctions across ANZ/ASEAN ongoing
6G (Future)	Sub-THz (100–1000 GHz)	2–10 GHz	Research phase only

 

5.6 Spectrum Licensing Approaches

5.6.1 Auction-Based Allocation

• Common in ANZ (e.g., Australian 3.6 GHz 5G auction, 2018).
• Generates government revenue but may increase operator costs → higher end-user tariffs.

5.6.2 Administrative Assignment

• Seen in some ASEAN countries with less mature regulatory markets.
• Spectrum directly assigned to incumbents.

5.6.3 Unlicensed Spectrum

• ISM bands (2.4 GHz, 5 GHz, 6 GHz) widely used for Wi-Fi and LoRaWAN.
• ASEAN has mixed adoption of 6 GHz Wi-Fi 6E.
• ANZ allows wider unlicensed use due to advanced regulatory frameworks.

5.6.4 Shared Spectrum Models

• CBRS (Citizens Broadband Radio Service)-style models in the US inspire ASEAN/ANZ regulators.
• Potential use of shared spectrum for private 5G/IoT deployments.

 

 

5.7 Spectrum Refarming

Refarming is essential to migrate legacy networks without wasting spectrum:

• ASEAN Example: Philippines’ Globe Telecom reallocated 2G/3G bands to LTE and now to 5G.
• ANZ Example: Telstra and Optus shut down 2G by 2016–2017, refarming spectrum to LTE.

Refarming balances backward compatibility with forward innovation.

 

5.7.1 ASEAN Spectrum Landscape

ASEAN nations are diverse in spectrum usage:

Singapore (IMDA): Aggressive 5G rollout in 3.5 GHz and mmWave. 900 MHz used for LTE fallback.

Malaysia (MCMC): Single Wholesale Network for 5G (controversial).

Thailand (NBTC): Multi-band 5G auctions (700 MHz, 2600 MHz, 26 GHz).

Indonesia: Large geography, limited 3.5 GHz spectrum availability → reliance on 1800/2100 MHz refarming.

Philippines: New entrant Dito Telecommunity licensed in 700 MHz for coverage.

Challenges:

Fragmented policies across countries.

High auction fees vs. affordability pressures.

Legacy 2G networks still operational in some regions.

 

 

5.7.2 ANZ Spectrum Landscape

Australia and New Zealand benefit from more coordinated spectrum planning.

Australia (ACMA):

o    700 MHz (“digital dividend”) allocated for LTE and 5G.

o    3.6 GHz allocated to 5G.

o    mmWave auctions (26 GHz) completed in 2021.

New Zealand (MBIE):

o    600 MHz, 700 MHz for rural LTE/5G.

o    3.5 GHz as the primary 5G band.

Unique features:

Large landmass → heavy reliance on low-band spectrum for rural coverage.

Early adoption of satellite augmentation (Starlink, Inmarsat) to fill coverage gaps.

 

5.8 Sub-THz and mmWave Challenges

While mmWave (24–40 GHz) offers extreme bandwidth, coverage limitations are significant:

• Propagation Loss: Free-space path loss increases 6 dB per frequency doubling.
• Penetration Loss: High absorption through walls, foliage, even rain.
• Deployment Cost: Requires dense small-cell infrastructure.

ASEAN context: Dense cities like Singapore may benefit. Rural ASEAN less suitable.
ANZ context: Limited mmWave beyond urban hotspots due to geography.

Sub-THz for 6G introduces further challenges: atmospheric absorption, hardware cost, regulatory uncertainties.

 

5.9 Spectrum Policy Trends

• Harmonization: ITU encourages global harmonization (e.g., 3.5 GHz for 5G), but national variations persist.
• Dynamic Spectrum Sharing (DSS): Used to allow LTE and 5G coexistence in same band.
• Satellite-Terrestrial Convergence: New ITU allocations for non-terrestrial networks (NTN) under 5G NR.
• AI for Spectrum Management: Cognitive radio and ML-driven spectrum sensing proposed for 6G.

 

5.10 Summary

Spectrum regulation has been both an enabler and bottleneck of mobile technology evolution. The 900 MHz band exemplifies the continuity of spectrum reuse, while higher bands (3.5 GHz, mmWave) reflect the demand for capacity. ASEAN’s fragmented policies contrast with ANZ’s coordinated planning, yet both regions demonstrate innovation in refarming, auctions, and satellite integration.

AI and Data Science will increasingly be used for dynamic spectrum allocation, interference detection, and demand prediction, making spectrum management not only a regulatory challenge but also a computational one.

 

 

 

 

 

 

 

 

 

 

 

END OF CHAPTER 5

 

Chapter 5.11  AI and ML for Propagation Prediction

Traditional models are deterministic/empirical. AI/ML methods use real measurement data:

• Supervised ML: Train models on drive-test/KPI data to predict RSRP/RSRQ.
• Deep Learning: CNNs for RF fingerprinting.
• Reinforcement Learning: Adaptive beam alignment in mmWave links.
• Big Data Analytics: Correlating traffic patterns with propagation anomalies.

Operators in Singapore and Australia have trialed AI for automatic coverage hole detection.

 

Summary

Radio propagation and link budgets are the foundation of mobile system design. Lower frequencies (e.g., 900 MHz) provide wide-area coverage, while higher frequencies (e.g., mmWave) require AI-optimized dense deployments. Modern operators cannot rely solely on mathematical models—they require data-driven, AI-augmented approaches to continuously optimize networks in real time.

 

Chapter 6 – Modulation, Coding, and Multiple Access Evolution

 

6.1 Introduction

The evolution of modulation, coding, and multiple access techniques has been the engine driving improvements in capacity, spectral efficiency, and reliability across successive mobile generations. Each leap in wireless standards has corresponded to advancements in how bits are represented on radio waves and how multiple users are multiplexed onto finite spectrum.

This chapter explores these advancements chronologically, from analog FM in 1G to OFDMA and advanced coding in 5G, while considering the growing role of AI and ML in waveform optimization, adaptive coding, and interference management.

6.2 Modulation Evolution

1G: Analog FM

• Frequency Modulation (FM): Simple but inefficient.
• Channel bandwidth: 25–30 kHz.
• Susceptible to interference, poor spectral efficiency.

2G: Digital Modulation

• GMSK (Gaussian Minimum Shift Keying) in GSM.
• Constant envelope → efficient power amplifiers.
• Robust but limited spectral efficiency (~1 bit/s/Hz).
• π/4-QPSK in IS-54 TDMA.
• QPSK/BPSK/CDMA spreading in IS-95.

2.5G and 3G

• 8-PSK in EDGE → 3 bits/symbol.
• QPSK, 16-QAM, 64-QAM in W-CDMA/HSPA.
• Adaptive modulation introduced: switching based on channel conditions.

 

4G LTE (Long  Term Evolution)

• OFDMA (downlink): Combats multipath with cyclic prefix.
• SC-FDMA (uplink): Reduces Peak-to-Average Power Ratio (PAPR).
• Adaptive modulation up to 256-QAM.

5G NR

• Flexible numerologies (subcarrier spacing 15–240 kHz).
• Higher-order QAM (up to 1024-QAM in trials).
• Beamformed modulation optimized for spatial channels.

6G Outlook

• Orbital Angular Momentum (OAM) waves under research.
• AI-designed waveforms: Neural networks optimize modulation schemes dynamically.

 

 

6.3 Channel Coding Evolution

Error-correcting codes have evolved alongside modulation, crucial for reliability under fading and interference.

• 1G: No channel coding.
• 2G GSM: Convolutional coding + interleaving.
• 3G UMTS: Turbo codes → near Shannon-limit performance.
• 4G LTE: Turbo codes (legacy), later LDPC (in trials).
• 5G NR:
• LDPC (Low-Density Parity Check): For data channels.
• Polar Codes: For control channels.
• 6G (Research): Sparse graph codes, AI-assisted error correction.

 

6.4 Multiple Access Techniques

1G – FDMA – Frequency Divided Multiple Access Technology

• Users separated by frequency.
• Simple but inefficient, poor scalability.

2G – TDMA & CDMA – Time Divided & Code Division Multiple-Access

• TDMA (Time Division Multiple Access):
• GSM: 200 kHz channels, 8 time slots.
• Effective for circuit-switched voice.
• CDMA (Code Division Multiple Access):
• IS-95, UMTS.
• Spreads signals with pseudo-random codes.
• Soft handoffs, interference-limited planning.

 

3G – W-CDMA

• Wideband CDMA, 5 MHz carriers.
• Supported higher data rates but suffered from self-interference.

OFDMA

• Orthogonal Frequency Division Multiple Access.
• Divides spectrum into orthogonal subcarriers.
• Flexible scheduling in time–frequency grid.
• Multipath resilience, high spectral efficiency.

5G NR – Flexible OFDM

• Variable subcarrier spacing.
• OFDM extended to mmWave, massive MIMO.
• Sidelink and non-terrestrial integration.
• Support for URLLC (low latency), mMTC (IoT), eMBB (broadband).

6G Outlook

• NOMA (Non-Orthogonal Multiple Access):
• Users multiplexed by power domain.
• ML used for successive interference cancellation.
• Rate-Splitting Multiple Access (RSMA).
• AI-driven resource allocation predicted to dominate.

 

 

6.5 Synchronization and Reference Signals

Modern systems require precise synchronization for coherent detection.

• 2G GSM: Broadcast Control Channel (BCCH).
• 3G: Primary/Secondary Synchronization Channels.
• 4G LTE: Primary/Secondary Sync Signals, Cell-specific Reference Signals.
• 5G NR:
• PSS/SSS for cell search.
• DM-RS/CSI-RS for channel estimation in beamformed channels.

AI-enhanced synchronization techniques are being explored, where ML algorithms dynamically track Doppler and fading.

 

6.6 AI and Data Science in Modulation and Coding

• Adaptive Modulation & Coding (AMC): Traditional link adaptation uses SNR  (Signal Noise Ratio) thresholds. AI-based methods optimize multi-dimensional trade-offs (throughput, reliability, latency).
• Deep Learning for Decoding: Neural decoders outperform LDPC in some cases.
• Reinforcement Learning for Multiple Access: Used for NOMA power allocation.
• Waveform Design: AI models search modulation/coding combinations beyond human-designed standards.

 

 

6.7 Summary

From simple analog FM (1G) to AI-optimized OFDM and coding in 5G and beyond, modulation and multiple access have been central to increasing capacity and efficiency. While human-designed techniques dominated until 5G, the 6G era will increasingly rely on AI to co-design and optimize waveforms, coding, and access schemes in real time.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

END OF CHAPTER 6

Chapter 7 - Network Topologies and Hierarchies

 

7.1  Introduction

A mobile network is not only defined by its air interface or modulation scheme but also by its topology — the physical and logical arrangement of base stations, cells, and user devices. Over time, as demand for coverage, capacity, and reliability has increased, mobile networks have evolved from simple macrocellular deployments to heterogeneous networks (HetNets) integrating macro, micro, pico, femto cells, mesh, and device-to-device (D2D) topologies.

In parallel, Low-Power Wide Area Networks (LPWANs) such as LoRaWAN introduced a different topology optimized for low data rate, long-range IoT connectivity.

This chapter examines the evolution of topologies, their trade-offs, and the role of AI and data science in self-organizing networks (SONs) and autonomous topology management.

 

7.2  Macrocellular Topology

Definition

Macrocells are large coverage areas served by high-power base stations, typically transmitting at 20–40 W, mounted on towers or rooftops.

Characteristics

• Coverage radius: 2–35 km depending on frequency and terrain.
• Primary layer of 1G–4G deployments.
• Cost-effective for rural areas (ANZ’s vast geography).

Limitations

• Inefficient for indoor coverage in dense urban environments.
• High inter-cell interference in congested networks.

 

7.3 Microcells

Definition

Smaller cells (100 m – 2 km radius) deployed for capacity enhancement in high-traffic areas.

Use Cases

• Urban hotspots (shopping malls, downtown cores).
• Supplement macro coverage in ASEAN megacities like Bangkok, Manila, Jakarta.

Trade-offs

• Lower transmit power → lower CAPEX per site.
• Increased OPEX due to denser site requirements.

 

 

6.4 Picocells

Definition

Very small base stations with coverage radius of 10–200 meters.

Use Cases

• Enterprises, office buildings, airports.
• Often used for indoor LTE/5G private networks.

ASEAN/ANZ Deployment

• Singapore uses picocells for indoor 5G coverage in MRT tunnels.
• Australia: mining companies deploy picocells for private LTE in remote facilities.

 

6.5 Femtocells

Definition

Ultra-small, consumer-deployed base stations (coverage radius 10–50 m).

Role

• Extend indoor coverage.
• Use residential broadband as backhaul.

Challenges

• Interference management.
• Security of user-deployed nodes.

 

6.6 Heterogeneous Networks (HetNets)

HetNets combine macro, micro, pico, and femto cells into a layered architecture.

Benefits

• Macro layer ensures coverage.
• Small cells increase localized capacity.
• Offloading traffic improves spectrum efficiency.

6.7 SON (Self-Organizing Networks)

• Automates configuration and optimization.
• AI-based SONs adjust neighbor relations, power levels, and handover thresholds dynamically.
• Widely used in 4G/5G deployments in ANZ (Telstra’s AI-driven SON pilot).

6.8 Mesh and Ad Hoc Networks

Public Safety and Tactical Use

• TETRA systems use mesh/ad hoc extensions for first responders. (Used in Singapore for Oil & Gas, specific to Bukom & Jurong Islands)
• ProSe (Proximity Services): Introduced in LTE Rel-12 for device-to-device.

Military and Industrial IoT

• Mesh networks in mining (Australia) and oil rigs (Malaysia).
• D2D communication critical for autonomous vehicles.

Challenges

• Interference and scalability.
• Security of decentralized topologies.

6.9 LPWAN Topologies

LoRaWAN – Low power Long Range Wide Area Network

Star-of-stars topology: end devices → gateways → network server.

Gateways connect to central servers via IP backhaul.

Optimized for long-range, low-power IoT telemetry.

Alternatives

• Sigfox: Ultra-narrowband star topology.
• NB-IoT / LTE-M: Cellular LPWAN alternatives.

ASEAN/ANZ Adoption

Singapore: nationwide LoRaWAN IoT deployments for smart city.

New Zealand: farmers use LoRaWAN for livestock monitoring.

6.10 AI/ML in Topology Optimization

Cell Planning

• AI models trained on traffic data predict where small cells should be deployed.
• Reinforcement learning for automatic tilt optimization of antennas.

Traffic Load Balancing

• Clustering algorithms (k-means, DBSCAN) detect overloaded cells.
• Load shifted via AI-controlled handover tuning.

Failure Recovery

• Predictive maintenance using anomaly detection.
• Self-healing topologies reroute traffic around failed nodes.

 

 

6.11 Comparative Topology Trade-offs

Topology	Coverage Radius	Strengths	Limitations
Macrocell	2–35 km	Wide area coverage, cost-effective	Poor indoor penetration
Microcell	0.1–2 km	Capacity boost, urban use	Higher site density
Picocell	10–200 m	Indoor coverage, enterprise	CAPEX for enterprise
Femtocell	<50 m	Consumer indoor use	Security, interference
Mesh/Ad Hoc	Varies	Resilient, decentralized	Complexity, interference

6.12 Summary

Network topologies evolved from macro-only 1G deployments to sophisticated multi-layered 5G HetNets and specialized IoT topologies. In ASEAN, dense cities require pico/femto deployments, while ANZ emphasizes macrocells and satellite integration.

AI and Data Science enable dynamic optimization of these topologies, ensuring real-time adaptability to changing traffic, failures, and propagation environments.

 

 

END OF CHAPTER 6

 

 

 

Chapter 7 - Standards by Generation

(1G to 5G, Wi-Fi, WiMAX, LoRaWAN)

 

7.1 Introduction

Telecommunications systems evolve not in isolation but under the guidance of standards bodies, which ensure interoperability, economies of scale, and global compatibility. The most influential standards bodies are:

• 3GPP (3rd Generation Partnership Project): LTE, 5G NR, core networks.
• ITU (International Telecommunication Union): Defines IMT-2000, IMT-Advanced, IMT-2020 (frameworks for 3G, 4G, 5G).
• IEEE (Institute of Electrical and Electronics Engineers): Wi-Fi (802.11), WiMAX (802.16).
• LoRa Alliance: LoRaWAN standardization for LPWAN.

This chapter provides a generation-by-generation breakdown of standards, followed by Wi-Fi/WiMAX and LoRaWAN ecosystems.

 

7.2 1G Standards (Analog Cellular)

AMPS (Advanced Mobile Phone System)

• Launched in the US in 1983.
• 30 kHz FM channels in the 800 MHz band.
• FDMA, no encryption, poor spectral efficiency.

NMT (Nordic Mobile Telephony) – NoKIA/NorTEL

• First international cellular standard (Scandinavia, early 1980s).
• 450/900 MHz bands.
• Influenced global adoption.

 

TACS (Total Access Communication System)

• UK adaptation of AMPS.
• Widely used in Europe and Asia before GSM.

Summary: 1G standards were fragmented, regional, and analog-only.

 

 

7.3  2G Standards (Digital Cellular)

GSM (Global System for Mobile Communications)

• ETSI standard (Europe, 1991).
• TDMA, 200 kHz carriers.
• Global adoption, especially in ASEAN/ANZ.
• Introduced SMS and encryption.

IS-95 (cdmaOne)

• Qualcomm’s CDMA-based system.
• Spread spectrum, soft handoffs.
• Popular in US, Korea.

IS-136 (D-AMPS)

• US TDMA system.
• Transitional, eventually replaced by GSM/CDMA.

2.5G Enhancements

• GPRS: Packet-switched data (~50 kbps).
• EDGE: Enhanced modulation (8-PSK), up to ~200 kbps.

 

 

7.4 3G Standards (IMT-2000)

UMTS / W-CDMA

• Europe and Asia.
• 5 MHz carriers, 2.1 GHz band.
• HSPA/HSPA+ → up to 42 Mbps.

CDMA2000

• Evolution of IS-95.
• 1xRTT, EV-DO.
• Deployed in North America, some Asian markets.

Regional Adoption

• ASEAN: uneven rollout due to cost.
• ANZ: Telstra’s “Next G” (850 MHz) was one of the most advanced UMTS networks globally.

 

 

7.5  4G Standards (IMT-Advanced)

LTE (Long Term Evolution)

• 3GPP Release 8 (2009).
• OFDMA (DL), SC-FDMA (UL).
• Up to 20 MHz per carrier; carrier aggregation (CA) enabled multi-carrier operation.
• LTE-Advanced (Rel-10+):
• Higher-order MIMO (8x8).
• Coordinated multipoint (CoMP).
• Peak rates >1 Gbps.

WiMAX (IEEE 802.16e/m)

• Considered 4G by ITU, but lost to LTE due to ecosystem dominance.
• Still deployed in some ASEAN regions for fixed broadband.

 

7.6 5G Standards (IMT-2020)

3GPP Release 15–17

• 5G NR (New Radio).
• Flexible numerology (15–240 kHz spacing).
• Sub-6 GHz (FR1) and mmWave (FR2).
• Massive MIMO, beamforming.
• NSA (non-standalone, LTE anchor) and SA (standalone, 5G core).

Release 18 and 5G Advanced

• AI-native RAN.
• Enhanced XR (Extended Reality) support.
• RedCap (Reduced Capability) devices for IoT.
• Non-terrestrial networks (NTN): satellites integrated with 5G.
  

 

7.7 Wi-Fi Standards (IEEE 802.11 Family)

 

Generation	Standard	Year	Features
Wi-Fi 1	802.11b	1999	DSSS, 2.4 GHz, 11 Mbps
Wi-Fi 2	802.11a	1999	OFDM, 5 GHz, 54 Mbps
Wi-Fi 3	802.11g	2003	2.4 GHz, OFDM, 54 Mbps
Wi-Fi 4	802.11n	2009	MIMO, 2.4/5 GHz, 600 Mbps
Wi-Fi 5	802.11ac	2014	MU-MIMO, 160 MHz channels, Gb/s
Wi-Fi 6	802.11ax	2019	OFDMA, BSS Coloring, low latency
Wi-Fi 7	802.11be	2024 (ongoing)	320 MHz channels, multi-link operation (MLO)

 

 

ASEAN/ANZ: Wi-Fi complements cellular for indoor connectivity; Wi-Fi 6E in 6 GHz is adopted in Australia and Singapore.

 

LoRaWAN (Low-Power Wide Area Network)

Characteristics

• Physical Layer: LoRa modulation (chirp spread spectrum).
• Topology: Star-of-stars (end devices → gateways → network servers).
• Bands: ISM (868/915 MHz in ANZ/ASEAN).

Device Classes

• Class A: Ultra-low power, scheduled downlinks.
• Class B: Periodic beacon windows.
• Class C: Almost continuous receive.

Deployment

• ASEAN: Smart city (Singapore, Malaysia).
• ANZ: Agricultural IoT (livestock, irrigation monitoring).
  
  

7.8 Comparative Technology Matrix

 

Technology	Access Method	Peak Data Rate	Latency	Strengths	Limitations
GSM/EDGE	TDMA	~0.2 Mbps	~150 ms	Wide coverage	Low data rates
UMTS/HSPA	W-CDMA	14–42 Mbps	50–100 ms	Proven tech	Interference mgmt
LTE-A	OFDMA/SC-FDMA	>1 Gbps	20–50 ms	Ecosystem, efficiency	Dense planning
5G NR	OFDMA	>10 Gbps	<1 ms	Flexible, slicing, URLLC	Complexity, mmWave limits
Wi-Fi 6/7	OFDMA	Multi-Gbps	~10 ms	Unlicensed, cheap	Contention, interference
LoRaWAN	ALOHA-like	kbps	Seconds	Long range, ultra-low power	Very low throughput

7.9 Summary

 

Standards have progressed from analog fragmentation (1G) to globally harmonized digital ecosystems (5G NR). Wi-Fi and LoRaWAN evolved in parallel, targeting different niches: Wi-Fi for indoor broadband and LoRaWAN for ultra-low power IoT.

The unifying trend is toward flexible, AI-native, software-defined standards where modulation, access, and resource allocation can adapt in real time. This lays the groundwork for 6G, where AI will likely play a direct role in waveform and standard evolution.

END OF CHAPTER 7

 

 

 

Chapter 8 - Specialized Systems & Use Cases

(LoRaWAN, TETRA ATEX, ST GRID, Satellites: Beidou, Gouwang, Qianfan, Starlink, Inmarsat)

 

8.1 Introduction

Beyond mainstream mobile standards (1G–5G), specialized wireless systems have emerged to meet unique requirements:

• Low-power IoT (LoRaWAN).
• Mission-critical communications (TETRA ATEX, ST GRID).
• Satellite-based systems (Beidou, Gouwang, Qianfan, Starlink, Inmarsat).

These systems fill niches not well served by cellular broadband: industrial safety, rural connectivity, global positioning, maritime/aviation communications, and massive IoT telemetry.

 

8.2 LoRaWAN (Low Power, Long Range IoT)

Technical Overview

• LoRa (physical layer): Chirp spread spectrum modulation.
• LoRaWAN (protocol): Defines MAC and backend.
• Bands: ISM (EU 868 MHz, US/AU 915 MHz, ASEAN varies).
• Data rate: 0.3–50 kbps.
• Range: 2–15 km urban, >50 km rural line-of-sight.
• Battery life: 5–10 years.

Use Cases

• Smart cities: parking sensors, waste management.
• Agriculture: soil moisture, livestock tracking (ANZ).
• Disaster response: temporary sensor networks.

Pros and Cons

✅ Long range, ultra-low power, unlicensed spectrum.
❌ Low throughput, high latency (seconds).

8.3 TETRA ATEX Systems

TETRA (Terrestrial Trunked Radio)

• ETSI standard for professional mobile radio.
• Used by police, emergency services, utilities.
• Digital trunking, encryption, direct mode (D2D).
• 446Mhz in Singapore

ATEX Compliance

• Devices certified for explosive environments (oil rigs, chemical plants, mining).
• Ruggedized walkie-talkies with intrinsically safe design.

ASEAN/ANZ Deployment

• ASEAN: TETRA used in airports, seaports, public safety (Singapore Police, Jakarta MRT).
• ANZ: ATEX radios in mining (Western Australia, Queensland coal sector).

 

Pros and Cons

 

✅ Mission-critical reliability, D2D, encrypted.
❌ Limited bandwidth, not suited for broadband data. Transmitter is ranged up to 10km radius

 

 

 

8.4 -  ST GRID Walkie-Talkie Phone

 

Overview

• Hybrid of mobile phone and push-to-talk (PTT) radio.
• Developed for industrial/public safety environments.
• Operates on both cellular and dedicated PMR spectrum. 800/850x Mhz Freq in Singapore

Features

• Group calls with near-zero latency.
• Ruggedized design.
• Integration with LTE mission-critical push-to-talk (MCPTT).

ASEAN/ANZ Use Cases

• ANZ: Utilities and railway operators use ST GRID devices for workforce communication.
• ASEAN: Oil & gas industry, metro projects (e.g., Malaysia’s MRT).

Pros and Cons

✅ Instant communication, hybrid cellular/PMR.
❌ Niche, not scalable for broadband IoT.

 

 

8.5 Satellite Systems

 

Beidou (China)

• Global Navigation Satellite System (GNSS).
• Provides positioning, navigation, timing (PNT).
• Coverage: Global since 2020 (BDS-3).
• ASEAN impact: integrated into agriculture and logistics.

Gouwang (China’s broadband LEO project)

• LEO constellation under development.
• Target: broadband satellite internet, competitor to Starlink.

Qianfan

• Another Chinese LEO initiative for global internet coverage.
• Focus on integration with Belt & Road countries, including ASEAN.

Starlink (SpaceX, USA)

• LEO constellation (>5,000 satellites deployed as of 2024).
• Low latency (~25–40 ms).
• Broadband speeds 50–250 Mbps.
• ASEAN: Pilots in Philippines, Malaysia.
• ANZ: Widely used in rural Australia/New Zealand for broadband.

Inmarsat (UK)

• Legacy GEO satellite operator.
• Provides maritime, aviation, emergency communications.
• Lower data rates, higher latency (~600 ms).

 

System	Orbit/Layer	Latency	Strengths	Limitations
Beidou	MEO/GEO	~50–100 ms	GNSS PNT	Not broadband
Gouwang/Qianfan	LEO (planned)	~25–40 ms	National broadband strategy	Still unproven
Starlink	LEO	25–40 ms	Broadband, rural coverage	Expensive, high CAPEX
Inmarsat	GEO	~600 ms	Aviation, maritime	High latency, limited throughput
LTE/5G	Terrestrial	1–20 ms	High capacity	Limited rural reach
LoRaWAN	Terrestrial ISM	Seconds	Ultra-low power IoT	Very low throughput
TETRA ATEX	PMR	<100 ms	Mission-critical voice	No broadband

 

 

 

8.6 Comparative Analysis: Satellite vs Terrestrial

AI/ML in Specialized Systems

• LoRaWAN: ML-based anomaly detection in IoT sensor data.
• TETRA ATEX: AI-based predictive maintenance of mission-critical radios.
• Satellites:
• Beam-hopping optimization using AI.
• Traffic load prediction in LEO constellations.
• AI-based GNSS error correction (multipath mitigation).

 

ASEAN and ANZ Regional Use Cases

• ASEAN:
• Singapore: LoRaWAN smart nation projects.
• Philippines: Starlink trial for rural schools.
• Indonesia: Beidou adoption in logistics/shipping.
• ANZ:
• Australia: Starlink integrated into Telstra partnerships.
• New Zealand: LoRaWAN for agricultural IoT.
• Mining/oil industries: ATEX walkie-talkies.

 

8.7 Summary

Specialized systems illustrate the breadth of wireless applications beyond mainstream 5G.

• LoRaWAN dominates low-power IoT.
• TETRA ATEX / ST GRID serve mission-critical industrial/public safety needs.
• Satellites (Beidou, Starlink, Inmarsat) extend coverage where terrestrial fails.

Together, they form an ecosystem of complementary technologies. AI/ML ensures their integration into a seamless connectivity fabric for ASEAN and ANZ.

 

END CHAPTER 8

 

 

 

Chapter 9  - Core Networks: From MSC to 5GC with AI Enhancements

 

9.1 Introduction

While the Radio Access Network (RAN) defines how users connect to the network, the core network provides intelligence, switching, and control that enable mobility, authentication, and services. Core networks have evolved from circuit-switched telephony systems in 1G/2G to the all-IP, cloud-native, service-based architecture (SBA) of 5G.

This chapter explores core network evolution, with emphasis on:

• MSC (Mobile Switching Centers) in 1G/2G.
• SGSN/GGSN in 3G.
• EPC (Evolved Packet Core) in LTE.
• 5GC (5G Core), cloud-native and AI-enabled.

We also highlight AI, data science, and ML integration in traffic engineering, security, and self-healing.

 

 

9.2 1G/2G Core Networks – Circuit Switching

Mobile Switching Center (MSC)

• Centralized circuit switch.
• Handles call setup, routing, and handovers.
• Connected to PSTN for voice services.

Home/Visitor Location Registers (HLR/VLR)

• HLR: Stores subscriber identity and authentication keys.
• VLR: Temporary database for roaming subscribers.

Authentication Center (AuC)

• Stores secret keys for SIM-based authentication.

Short Message Service Center (SMSC)

• Enabled SMS delivery.

Limitations: No packet data, scalability issues, inflexible.

 

 

9.3 3G Core Networks – Dual Circuit and Packet

 

Serving GPRS Support Node (SGSN)

• Routes data packets.
• Tracks mobility for packet-switched services.

Gateway GPRS Support Node (GGSN)

• Connects packet data traffic to external networks (e.g., internet).

Circuit/Packet Split

• Dual domains coexisted: circuit (voice) + packet (data).

Impact: First step toward always-on data connections, though still limited compared to LTE.

 

9.4 4G Core Networks – The Evolved Packet Core (EPC)

EPC Architecture

• MME (Mobility Management Entity): Controls mobility, authentication.
• SGW (Serving Gateway): Routes user traffic.
• PGW (Packet Gateway): Connects to external IP networks.
• PCRF (Policy and Charging Rules Function): Manages QoS and charging

All-IP Architecture

• Voice over LTE (VoLTE) through IMS (IP Multimedia Subsystem).
• Circuit switching eliminated.

Benefits

• Reduced latency.
• Scalability for broadband.
• Network sharing and virtualization enabled.

 

9.5G Core (5GC) – Service-Based Architecture

Service-Based Architecture (SBA)

• Components exposed as services via APIs.
• Cloud-native, microservices architecture.

Key Functions

• AMF (Access and Mobility Management Function): Manages user access and mobility.
• SMF (Session Management Function): Session setup, QoS policies.
• UPF (User Plane Function): Handles user traffic forwarding.
• NRF (Network Repository Function): Service discovery.
• PCF (Policy Control Function): Policy management.
• AUSF (Authentication Server Function): Authentication and security.

 

Features

• Network Slicing: Logical partitioning for different services (e.g., URLLC vs eMBB).
• Edge Computing (MEC): Applications hosted at the edge to reduce latency.
• Non-Terrestrial Networks (NTN): Satellites integrated with 5GC.

 

9.6 Virtualization and Cloud-Native Cores

• NFV (Network Function Virtualization): Replaces proprietary hardware with virtualized functions.
• SDN (Software Defined Networking): Separates control/data planes, enabling programmability.
• Containers and Kubernetes: Used in 5GC for cloud-native deployments.

 

9.7 AI and Data Science in Core Networks

7.7.1 Traffic Prediction and Optimization

• ML models forecast traffic load, enabling dynamic scaling of UPFs.
• Time-series models (ARIMA, LSTM) predict busy-hour congestion.

7.7.2 Network Slicing Management

• AI orchestrators dynamically allocate slices.
• Reinforcement learning optimizes slice performance.

7.7.3 Security and Anomaly Detection

• ML-based intrusion detection identifies unusual traffic patterns.
• AI-based fraud prevention (SIM cloning, DDoS mitigation).

7.7.4 Fault Management and Self-Healing

• Predictive maintenance of virtual network functions (VNFs).
• AI-based failure prediction in data centers.

 

9.8 ASEAN and ANZ Deployment Trends

ASEAN

• Singapore: early adoption of 5GC standalone networks.
• Malaysia: wholesale 5G model integrates 5GC for multiple operators.
• Indonesia/Philippines: NSA rollouts dominate, limited standalone due to cost.

ANZ

• Australia: Telstra deploying cloud-native 5GC with edge computing for industrial IoT.
• New Zealand: Spark focusing on MEC for smart agriculture.

 

9.9 Summary

The core network has evolved from rigid circuit-switching (MSC) to flexible, AI-native service-based architectures (5GC).

• 1G/2G: voice-centric, circuit-switched.
• 3G: dual circuit + packet.
• 4G: all-IP EPC.
• 5G: service-based, virtualized, sliceable, AI-enabled.

Future 6G cores will be autonomous, cognitive, and self-optimizing, with AI not as an overlay but as a native capability.

 

END CHAPTER 9

 

 

 

Chapter 10 - Security and Authentication Across Generations

 

10.1 Introduction

Security in mobile networks has evolved alongside the technologies themselves. While 1G networks offered virtually no protection against eavesdropping and fraud, today’s 5G networks provide end-to-end encryption, mutual authentication, and subscriber privacy mechanisms.

However, each generation introduced new attack surfaces: cloning in 1G, weak ciphers in 2G, signaling storms in 3G, IMS vulnerabilities in 4G, and virtualized network security risks in 5G. Looking ahead, AI/ML-enhanced security frameworks are expected to underpin 6G.

 

10.2 1G: Insecure Beginnings

• Analog FM voice: No encryption, easily intercepted by scanners.
• Authentication: None; phone cloning rampant (copying ESN/IMSI).
• Vulnerabilities:
• Fraudulent calls billed to victims.
• Eavesdropping in sensitive scenarios (e.g., law enforcement).

 

10.3 2G: Basic Digital Security

Authentication and Encryption

·         SIM-based authentication: Introduced secret key (Ki) stored in SIM and AuC.

·         A3/A8 algorithms: Generate session key (Kc).

·         Ciphering: A5/1, A5/2 stream ciphers.

Weaknesses

·         A5/1 eventually cracked with rainbow tables.

·         No mutual authentication (network not authenticated by handset) → man-in-the-middle attacks.

 

10.4 3G: Stronger Mutual Authentication

UMTS Authentication and Key Agreement (AKA)

·         Mutual authentication introduced.

·         128-bit keys.

·         Stronger algorithms (Kasumi cipher).

Security Gains

·         Protected against fake base stations.

·         Improved integrity protection.

Limitations

·         Still vulnerable to denial-of-service and jamming.

·         Signaling overload attacks began to emerge.

 

10.5 4G: IP-Based Security Challenges

EPS-AKA

·         Mutual authentication maintained.

·         Session keys for encryption/integrity on IP bearer.

VoLTE and IMS Vulnerabilities

·         SIP protocol in IMS exposed to spoofing, denial-of-service.

·         Interworking with internet services increased attack surface.

LTE-Specific Threats

·         IMSI Catchers (Stingrays): Exploited fallback to 2G/3G for interception.

·         Rogue eNodeBs: Fake base stations used for surveillance.

 

 

10.6 5G: Enhanced Security and Privacy

5G Authentication and Key Agreement (5G-AKA)

·         Based on EPS-AKA but enhanced.

·         Introduced SUCI (Subscription Concealed Identifier) to protect IMSI.

·         Home network authenticates user directly, reducing reliance on roaming operator.

Integrity Protection

·         Extended to user plane as well as control plane.

Network Slicing Security

·         Each slice may have independent security policies.

·         AI used for adaptive policy enforcement.

Virtualization and Cloud Risks

·         NFV/SDN components exposed to cloud vulnerabilities.

·         Zero-trust architectures emerging.

 

10.7 AI and Data Science for Security

Anomaly Detection

·         ML models trained on traffic patterns detect botnets, DDoS, or malware.

·         Supervised and unsupervised learning for intrusion detection.

Threat Prediction

·         Predictive analytics flag unusual signaling storms.

·         Reinforcement learning enhances adaptive firewalls.

Privacy-Preserving AI

·         Federated learning trains security models without centralizing sensitive data.

 

 

10.8 ASEAN and ANZ Security Considerations

ASEAN

·         Some ASEAN countries still operate 2G → vulnerabilities remain.

·         Increased risk from state and non-state actors exploiting outdated protocols.

·         Singapore leading in 5G cybersecurity regulation.

ANZ

·         Australia implemented 5G security bans on certain vendors citing supply chain concerns.

·         New Zealand adopting cloud security frameworks for 5GC deployments.

 

10.9 Comparative Evolution of Security

Generation	Auth Method	Encryption	Weaknesses
1G	None	None	Cloning, eavesdropping
2G	SIM-based (A3/A8)	A5/1, A5/2	No mutual auth, weak ciphers
3G	AKA	Kasumi	DoS, jamming
4G	EPS-AKA	AES/Snow3G	IMS vulns, rogue eNodeB
5G	5G-AKA	256-bit AES, integrity	NFV/SDN risks, slice complexity

 

 

10.10 Summary

Security has advanced from non-existent (1G) to mutual, encrypted, privacy-preserving (5G). Yet, vulnerabilities evolve with technology: virtualized, cloud-native 5G cores create new risks. AI and ML are indispensable in threat detection, anomaly analysis, and adaptive security.

In ASEAN/ANZ, the coexistence of legacy 2G with cutting-edge 5G creates unique regional security challenges.

 

END CHAPTER 10

 

Chapter 11 – Performance, QoS, and KPI Engineering

 

11.1 Introduction

Performance engineering ensures that mobile and wireless networks meet user expectations and service-level agreements (SLAs).

This involves measuring Key Performance Indicators (KPIs), enforcing Quality of Service (QoS) classes, and optimizing resources to balance coverage, capacity, and cost.

With the advent of AI and big data analytics, performance management has shifted from reactive monitoring to predictive, self-optimizing systems.

 

11.2 Fundamental Performance Metrics

11.2.1 Coverage Metrics

RSRP (Reference Signal Received Power): Measures coverage strength.

RSRQ (Reference Signal Received Quality): Signal quality relative to interference.

SINR (Signal-to-Interference-plus-Noise Ratio): Key for throughput estimation.

11.2.2 Capacity Metrics

Cell throughput (DL/UL).

Spectral efficiency (bps/Hz).

Number of active users per cell.

11.2.3 Reliability Metrics

Block Error Rate (BLER).

Call Drop Rate.

Packet Loss.

 

11.2.4 Latency and Jitter

Critical for real-time applications (VoIP, gaming, URLLC).

11.3 QoS Frameworks

11.3.1 2G/3G Era

Best-effort packet data in GPRS.

Circuit-switched QoS for voice.

11.3.2 4G LTE

QoS Class Identifier (QCI): Defines priority, delay, packet loss.

Differentiates services (e.g., VoLTE vs video streaming).

11.3.3 5G NR

• 5QI (5G QoS Identifier): Flexible, service-specific.

Example:

• URLLC: ultra-low latency, high reliability.
• eMBB: high throughput.
• mMTC: massive IoT connections.

11.4 KPI Engineering

9.4.1 Radio Access KPIs

Handover success rate.

RRC (Radio Resource Control) setup success.

PRB (Physical Resource Block) utilization.

11.4.2 Core Network KPIs

Session setup success rate.

Mean session duration.

Signaling load distribution.

 

11.4.3 Service KPIs

VoLTE MOS (Mean Opinion Score).

Video start time, buffering ratio.

IoT device availability.

11.5 AI and Data Science for Performance Optimization

11.5.1 Predictive Analytics

ML models (LSTM, ARIMA) forecast traffic loads.

Seasonal patterns detected in ASEAN/ANZ (e.g., urban rush hour vs rural IoT).

      11.5.2 Root Cause Analysis

Data clustering isolates fault patterns.

AI distinguishes between hardware failure vs interference vs misconfiguration.

11.5.3 Self-Optimizing Networks (SON)

Dynamic PCI allocation.

Automatic load balancing.

AI-based antenna tilt and power optimization.

11.5.4 QoE (Quality of Experience) Prediction

Data fusion: combine KPI data with crowdsourced app-level metrics.

Example: Telstra’s ML-driven app experience scoring.

 

11.6 ASEAN and ANZ Deployment Context

11.6.1 ASEAN

Dense cities (Jakarta, Manila, Bangkok): KPI engineering focused on congestion and interference mitigation.

Smart city rollouts (Singapore): QoS tuned for IoT and AR/VR applications.

11.6.2 ANZ

Rural coverage: KPIs focused on coverage continuity and satellite integration.

Mining/industrial IoT: SLAs prioritize machine telemetry reliability over throughput.

11.7 Example KPI Targets (5G in ASEAN/ANZ)

 

KPI	Target (Urban)	Target (Rural)
DL throughput	1 Gbps+	100 Mbps+
Latency	<10 ms	<20 ms
Handover success	>99%	>97%
Call drop rate	<0.5%	<1%
IoT availability	99.9%	99%

11.8 Case Studies

11.8.1 Singapore

AI-enhanced KPI monitoring in 5G standalone networks.

Network slicing deployed for autonomous vehicle trials.

11.8.2 Australia

Telstra using AI to balance capacity between urban eMBB and rural fixed wireless access (FWA).

Integration with Starlink for outback connectivity.

 

11.9 Challenges

• Data Overload: Billions of KPI data points daily → big data processing required.
• Heterogeneity: LTE/5G/LoRaWAN coexistence complicates KPIs.
• QoE Alignment: User experience often diverges from network-centric KPIs.
• Security Risks: Exposing KPI data pipelines may create vulnerabilities.

 

11.10 Summary

Performance engineering evolved from basic voice KPIs in 2G to multi-layered AI-driven frameworks in 5G.

• 2G/3G: best-effort packet data.
• 4G: structured QoS via QCIs.
• 5G: ultra-flexible QoS via 5QIs, AI-driven KPI monitoring.

ASEAN and ANZ deployments highlight contrasting needs: urban congestion vs rural coverage. AI and ML are now indispensable for ensuring QoE, optimizing KPIs, and predicting failures.

 

 

 

END OF CHAPTER 11

 

Chapter 12 – Deployment, Optimization, and Testing

 

12.1 Introduction

Building a mobile or wireless system is not just about spectrum and standards; deployment and optimization are the decisive factors that determine whether a network meets expectations.

From site acquisition and radio planning to drive testing and AI-assisted optimization, network engineering requires a structured methodology. With 5G and beyond, automation and data-driven intelligence dominate, reducing reliance on manual fieldwork.

 

12.2 Deployment Fundamentals

12.2.1 Site Selection

Coverage-driven (rural ANZ): Maximize cell radius, fewer sites.

Capacity-driven (urban ASEAN): Densify with small cells and HetNets.

Consider zoning, environmental impact, and backhaul availability.

12.2.2 Spectrum Planning

Lower bands (<1 GHz): coverage.

Mid-bands (2–4 GHz): balance.

High bands (>24 GHz mmWave): hotspots.

12.2.3 Backhaul

Microwave links (cost-effective in ASEAN rural).

Fiber (preferred in cities like Sydney, Singapore).

Satellite integration for remote ANZ deployments.

 

12.3 Optimization Techniques

12.3.1 Radio Optimization

Antenna tilt and azimuth adjustments.

Power control: Reducing interference, extending battery life.

Carrier aggregation balancing.

12.3.2 Interference Management

ICIC/eICIC (Inter-Cell Interference Coordination).

Coordinated multipoint (CoMP).

Beamforming calibration in 5G.

12.3.3 Load Balancing

Cell reselection parameters tuned dynamically.

Traffic steering between LTE, 5G, and Wi-Fi.

12.4 Testing Methodologies

12.4.1 Drive Testing

·         Traditional: engineers drive routes collecting RSRP, SINR, throughput.

·         Tools: TEMS, Nemo, Rohde & Schwarz scanners.

·         Challenge: costly, limited coverage.

12.4.2 Crowdsourced Testing

·         App-based KPI collection (Ookla, OpenSignal).

·         Offers broader coverage, lower cost.

·         Privacy and accuracy remain issues.

12.4.3 Lab Testing

·         Pre-deployment functional tests (conformance, stress, regression).

·         Simulated RAN and core environments.

 

12.4.4 Field Trials

·         Phased rollout in test markets.

·         Regulatory and safety approval required.

12.5 AI and Data Science in Optimization

12.5.1 Automated Parameter Tuning

·         AI systems self-adjust handover thresholds, antenna tilt, and power levels.

·         Reinforcement learning optimizes beamforming in dense urban ASEAN networks.

12.5.2 Predictive Network Planning

·         ML models forecast demand (e.g., holiday surges in Manila).

·         Clustering algorithms identify underperforming cells.

12.5.3 Fault Detection

·         AI detects anomalies in KPI streams.

·         Self-healing RAN reroutes traffic automatically.

12.5.4 Testing Automation

·         Digital twins simulate network rollout before deployment.

·         AI accelerates regression testing of core functions.

 

12.6 ASEAN and ANZ Deployment Context

12.6.1 ASEAN

·         Singapore: strong reliance on indoor small cells and HetNets.

·         Indonesia: challenge of archipelagic geography → satellite backhaul + microwave.

·         Vietnam/Thailand: government-driven 5G pilots with state-owned carriers.

12.6.2 ANZ

·         Australia: Telstra and Optus focus on nationwide coverage, hybrid satellite integration.

·         New Zealand: Spark targets agriculture IoT with precision deployment of LoRaWAN and LTE-M.

12.7 Comparative Testing Approaches

Method	Cost	Coverage	Accuracy	AI Integration
Drive Test	High	Limited	High	Low
Crowdsourced	Low	Wide	Moderate	High (data science pipelines)
Lab Testing	Medium	Simulated	High	High
Field Trial	Medium	Targeted	High	Medium

12.8 Challenges in Optimization

• Heterogeneous networks (HetNets): Complicated interference patterns.
• Multi-vendor environments: Interoperability issues.
• Cost constraints: Especially in emerging ASEAN markets.
• AI trustworthiness: Models must be explainable, not black-box.

 

12.9 Case Studies

12.9.1 Singapore 5G Standalone

·         Heavy reliance on AI-automated optimization.

·         Digital twin used for Marina Bay 5G rollout.

12.9.2 Australia Remote Connectivity

·         AI-driven planning of satellite-cellular hybrid networks.

·         Starlink integrated into outback schools with predictive traffic balancing.

12.10 Summary

Deployment and optimization evolved from manual field-driven engineering to AI-powered, data-driven automation.

• 2G/3G: manual drive testing and parameter tuning.
• 4G: semi-automated SONs.
• 5G: digital twins, AI-optimized RAN, predictive deployment.

In ASEAN, urban density and geographic fragmentation dominate deployment challenges. In ANZ, vast rural areas and industrial IoT drive reliance on automation and satellite integration.

END OF CHAPTER 12

 

 

Chapter 13 – Case Studies and Comparative Analysis

 

13.1 Introduction

Case studies illustrate how different mobile and wireless technologies have been applied in real-world contexts. Comparative analysis highlights strengths, weaknesses, and regional suitability across ASEAN and ANZ.

This chapter reviews use cases in cellular generations (2G–5G), Wi-Fi, LPWAN (LoRaWAN), mission-critical systems (TETRA ATEX, ST GRID), and satellite networks (Beidou, Starlink, Inmarsat, etc.).

 

13.2 Case Study 1 – GSM/2G in ASEAN (1990s–2000s)

Deployment

• GSM widely deployed in ASEAN from 1993 onward.
• 900/1800 MHz bands.
• Enabled SMS culture in Southeast Asia.

Impact

• First affordable digital voice in rural and urban areas.
• Led to economic transformation (mobile banking in Philippines).

Challenges

• Weak encryption (A5/1 cracked).
• Legacy persistence: 2G still active in parts of Myanmar, Cambodia.

 

13.3 Case Study 2 – UMTS/3G in ANZ (2005–2015)

Deployment

• Telstra’s Next G network (850 MHz) in Australia was world-leading.
• Extended 3G coverage across rural Australia.

Impact

• Internet access in previously isolated communities.
• Enabled agricultural IoT pilot projects.

Challenges

• High CAPEX for nationwide deployment.
• 3G sunset (2024–2025) creates migration challenges.

13.4 Case Study 3 – 4G LTE in ASEAN Megacities

Deployment

• Dense rollouts in Bangkok, Jakarta, Manila.
• Spectrum used: 1800 MHz, 2100 MHz, 2600 MHz.

Impact

• Mobile broadband penetration surged (>80% smartphone adoption).
• Gig economy (Grab, Gojek) flourished with LTE connectivity.

Challenges

• Congestion in dense urban environments.
• Interference management critical.

 

13.5 Case Study 4 – 5G in Singapore and Australia

Deployment

• Singapore: 5G standalone core with slicing (2020+).
• Australia: Telstra and Optus launched 5G FWA (Fixed Wireless Access).

Impact

• Singapore: 5G-enabled autonomous vehicles, smart ports.
• Australia: rural households gain high-speed internet via 5G FWA + Starlink.

Challenges

• ASEAN: patchy adoption, limited spectrum in some markets.
• ANZ: mmWave rollout limited by cost and coverage.

13.6 Case Study 5 – Wi-Fi 6 in ASEAN Enterprises

Deployment

• Wi-Fi 6 deployed in airports, universities, business parks.
• Complementary to 5G indoor deployments.

Impact

• Supports enterprise digitalization.
• Singapore: hybrid Wi-Fi/5G private networks in Changi Airport.

Challenges

• Congestion in 2.4 GHz band.
• Limited indoor spectrum coordination with cellular.

 

13.7 Case Study 6 – LoRaWAN in New Zealand Agriculture

Deployment

• LoRaWAN deployed for sheep/livestock monitoring, irrigation.
• Coverage: 10–15 km from base stations.

Impact

• Improved productivity and water efficiency.
• Reduced OPEX due to battery life (10 years+).

Challenges

• Low data rates (not suitable for multimedia telemetry).
• Dependence on unlicensed spectrum (interference risk).

 

13.8 Case Study 7 – TETRA ATEX in ASEAN Oil & Gas

Deployment

• ATEX-certified TETRA radios used on offshore rigs in Brunei, Malaysia.
• Mission-critical communication in hazardous zones.

Impact

• Safety compliance in flammable environments.
• Robust voice communications under extreme conditions.

Challenges

• Not suitable for data-heavy IoT.
• Proprietary devices with high cost.

13.9 Case Study 8 – Starlink in Rural ANZ

Deployment

• Starlink terminals installed in Australian Outback schools and New Zealand farms.
• Latency ~30–40 ms, throughput 50–200 Mbps.

Impact

• Bridged digital divide in rural education.
• Enabled telehealth and online education.

Challenges

• Cost of terminals (~USD 600) limits adoption.
• Weather sensitivity (rain fade).

 

11.10 Comparative Technology Table

Technology	Latency	Throughput	Strengths	Limitations	ASEAN/ANZ Use Case
GSM/EDGE	150+ ms	~0.2 Mbps	Coverage, maturity	Low data	SMS-driven culture in ASEAN
UMTS/HSPA	50–100 ms	~14–42 Mbps	Proven, flexible	Interference	Telstra Next G rural coverage
LTE	20–50 ms	100s Mbps–Gbps	Ecosystem, efficiency	Dense planning	ASEAN megacities broadband
5G NR	<10 ms	>1 Gbps	Low latency, slicing	Complexity	Singapore smart nation
Wi-Fi 6	<10 ms	Multi-Gbps	Cost-effective	Interference	Airports, enterprises
LoRaWAN	Seconds	kbps	Long range, IoT	Low throughput	NZ agriculture
TETRA ATEX	~100 ms	Narrowband	Mission-critical	Not broadband	Oil & gas ASEAN rigs
Starlink	30–40 ms	50–200 Mbps	Rural coverage	Cost, weather	ANZ schools & farms

13.11 AI/ML in Case Study Applications

• ASEAN megacities (LTE/5G): AI-driven interference coordination.
• ANZ rural Starlink: AI optimizes satellite beam allocation.
• LoRaWAN NZ farms: ML models predict soil irrigation schedules.
• Singapore 5G slicing: Reinforcement learning allocates slices dynamically.

 

13.12 Summary

Case studies demonstrate the complementary role of mobile, Wi-Fi, LPWAN, mission-critical, and satellite systems:

• 2G/3G laid foundations but persist as vulnerabilities.
• 4G/5G dominate urban ASEAN/ANZ broadband.
• Wi-Fi 6 complements indoor capacity.
• LoRaWAN thrives in agriculture IoT.
• TETRA ATEX ensures mission-critical safety.
• Starlink bridges rural broadband gaps.

AI/ML consistently enhances performance, reliability, and security across all case studies.

END OF CHAPTER 13

 

Chapter 15 – 5G Advanced and the Road to 6G

 

15.1 Introduction

While 5G networks are still being deployed globally, research and standardization have already shifted toward 5G Advanced (3GPP Release 18 and beyond) and 6G (expected 2030).

The focus is on making networks:

• AI-native (intelligence embedded into the protocol stack).
• Sustainable (green networking, energy efficiency).
• Ubiquitous (integrated terrestrial, aerial, and satellite).
• Convergent (communications, sensing, and computing combined).

 

15.2 5G Advanced (Release 18–20)

15.2.1 Key Enhancements

·         Extended MIMO: Extremely large antenna arrays with AI beam management.

·         Positioning Accuracy: Sub-meter precision for industrial IoT and autonomous vehicles.

·         XR Support: Optimized for AR/VR with latency <5 ms.

·         Uplink-Centric Design: Prioritizing sensor-heavy IoT and metaverse devices.

·         Non-Terrestrial Networks (NTN): Native satellite integration (LEO, GEO, HAPS).

15.2.2 AI-Native RAN

·         AI models trained to optimize scheduling, resource allocation, and interference coordination.

·         Network functions exposed as APIs for real-time ML training.

 

15.2.3 ASEAN/ANZ Applications

·         ASEAN: Smart factories in Malaysia, Thailand → low-latency positioning.

·         ANZ: Agriculture robotics with uplink-heavy IoT, powered by AI-optimized 5G.

 

15.3 Toward 6G (IMT-2030)

15.3.1 Target Capabilities

·         Data Rates: Up to 1 Tbps peak.

·         Latency: Sub-millisecond (0.1–0.5 ms).

·         Sensing: Integrated radar-like capabilities in radio beams.

·         AI-Native Architecture: AI/ML embedded at protocol design, not as an add-on.

·         Sustainability: Net-zero carbon networks.

15.3.2 Frequency Ranges

·         Sub-THz (100–300 GHz): Provides extreme bandwidth but limited range.

·         Visible Light Communication (VLC): Integration with optical systems.

15.3.3 Key Enablers

·         Reconfigurable Intelligent Surfaces (RIS): Smart reflective panels steering signals dynamically.

·         Extreme MIMO: Thousands of antenna elements.

·         Joint Communication and Sensing (JCAS): Networks double as radar/sensors.

·         Quantum-Safe Security: Resistant to post-quantum attacks.

 

 

15.4 AI and Data Science in 6G

• AI-Centric Design: Scheduling, waveform selection, and topology self-optimized.
• Digital Twins: Entire networks simulated in real-time for predictive optimization.
• Federated Learning: Privacy-preserving AI training across devices.
• Self-Evolving Networks: Reinforcement learning adapts protocols on the fly.

 

15.5 Non-Terrestrial Networks (NTN)

15.5.1 Satellite Integration

·         5G Advanced → 6G: Direct-to-device satellite service.

·         LEO constellations integrated with cellular core networks.

15.5.2 ASEAN/ANZ Relevance

·         ASEAN: Disaster recovery networks after typhoons/earthquakes.

·         ANZ: Outback IoT (mining, agriculture) seamlessly using NTN.

 

15.6 6G Use Cases

15.6.1 Industrial & Autonomous Systems

·         ASEAN: Autonomous ports in Singapore, Malaysia.

·         ANZ: Robotic farming with AI-powered real-time sensing.

15.6.2 Extended Reality (XR) & Metaverse

·         Holographic communication with Tbps uplink/downlink.

·         Seamless global presence for work and entertainment.

15.6.3 Smart Cities & Environmental Monitoring

·         ASEAN megacities: Real-time air pollution sensing + traffic management.

·         ANZ: Wildfire detection via UAV-6G integrated systems.

 

15.7 Comparative Timeline

Generation	Peak Rate	Latency	Key Innovations
4G LTE	1 Gbps	20–50 ms	All-IP, MIMO
5G NR	10 Gbps	<1 ms	Slicing, massive MIMO, NTN
5G Advanced	~20 Gbps	<0.5 ms	AI-native RAN, XR, precise positioning
6G	1 Tbps	0.1–0.5 ms	RIS, JCAS, sub-THz, AI-centric

15.8 Challenges

• Hardware Constraints: Sub-THz requires new semiconductor materials.
• Energy Consumption: Ultra-dense antennas must be energy-efficient.
• Standardization: Global consensus on IMT-2030 framework.
• Security: Quantum-resistant cryptography needed.

 

15.9 ASEAN and ANZ Roadmaps

15.9.1 ASEAN

·         Singapore, Thailand, Malaysia actively participating in ITU 6G studies.

·         Focus on smart cities, industrial IoT.

15.9.2 ANZ

·         Australia and New Zealand prioritizing agriculture, mining, and remote connectivity.

·         Early adoption of NTN for vast territories.

 

 

15.10 Summary

5G Advanced strengthens current 5G with AI-native features, uplink optimization, and satellite integration.

6G envisions AI as the network itself: Tbps speeds, near-zero latency, sensing + communications convergence.

ASEAN and ANZ will apply these differently:

·         ASEAN → dense urban smart cities, Industry 4.0.

·         ANZ → agriculture, mining, remote IoT with satellite integration.

END OF CHAPTER 15

 

 

Chapter 17 – Conclusion

 

17.1 Overview

This thesis traced the technical evolution of mobile and wireless systems from the analog 1G era of the 1980s to 5G and the trajectory toward 6G, while also exploring parallel ecosystems such as Wi-Fi, LoRaWAN, TETRA ATEX, ST GRID devices, and satellite networks (Beidou, Starlink, Inmarsat, Gouwang, Qianfan).

Across ASEAN and ANZ, the comparative analysis demonstrated that technology adoption reflects regional socio-economic and geographic realities:

• ASEAN megacities → dense LTE/5G rollouts, smart city integration.
• ANZ rural/remote regions → satellite and hybrid systems critical for inclusion.

 

17.2 Key Findings

17.2.1 Generational Progression

• 1G: Introduced cellular concept, but no security.
• 2G: Digitalization, SMS culture, basic security.
• 3G: Mobile broadband foundation, yet uneven global adoption.
• 4G LTE: All-IP, high capacity, foundation for the gig economy.
• 5G NR: AI-ready, ultra-low latency, slicing, satellite integration.

17.2.2 Specialized Systems

• LoRaWAN: Ideal for long-range IoT, but limited throughput.
• TETRA ATEX / ST GRID: Mission-critical, ruggedized comms for safety industries.
• Satellite Systems: Complement terrestrial, crucial for rural ANZ and disaster-stricken ASEAN.

 

17.2.3 AI and Data Science

Now central to:

Ø  Traffic prediction.

Ø  Security anomaly detection.

Ø  Automated optimization (SON, digital twins).

Ø  QoS and QoE assurance.

17.3 Regional Insights

ASEAN

• Diversity in deployment maturity: Singapore (5G advanced), Cambodia/Myanmar (legacy 2G still active).
• Smart city initiatives driving IoT adoption.
• Geographical fragmentation (archipelagos like Indonesia, Philippines) → satellite + terrestrial hybrids.

ANZ

• Vast landmass and low population density → heavy reliance on macrocells + satellite.
• Strong integration of LoRaWAN and LTE-M in agriculture.
• Starlink + Telstra partnerships extend digital equity in Australia’s Outback.

 

 

17.4 Future Outlook

17.4.1 5G Advanced

Ø  Strengthens positioning, uplink, XR, and NTN.

Ø  AI becomes intrinsic to RAN/core functions.

17.4.2 6G Vision

Ø  1 Tbps peak rates, 0.1 ms latency.

Ø  Integrated communication + sensing.

Ø  Reconfigurable Intelligent Surfaces (RIS), sub-THz spectrum.

Ø  Quantum-safe, AI-native networks.

ASEAN will leverage 6G for urban industrial automation, while ANZ will apply it to agriculture, mining, and remote connectivity.

 

17.5 Research Methodology Reflection

• Historical review: Standards evolution analyzed from 3GPP, ITU, IEEE documentation.
• Comparative analysis: Technologies benchmarked in terms of latency, throughput, security, and use cases.
• Case studies: ASEAN/ANZ deployments studied from operator whitepapers, government policies, and academic research.
• AI/ML integration: Evaluated through contemporary research on SONs, digital twins, and anomaly detection.

 

 

17.6 Final Thoughts

The central theme is convergence:

• Cellular, Wi-Fi, LPWAN, and satellite are not competing but complementary.
• AI/ML ensures networks become adaptive, autonomous, and predictive.
• Future 6G will be a cognitive fabric — sensing, computing, and communicating seamlessly.

The evolution from 1G to 5G and beyond is not merely technological but also socio-economic, shaping digital inclusion in ASEAN and ANZ. The challenge for researchers and policymakers is ensuring that technological sophistication aligns with accessibility, affordability, and security.

 

REFERENCES & BIBIOGRAPHY

 

[1]

Canonical: 3GPP TS 23.501, "System Architecture for the 5G System (5GS)"

URL: https://www.3gpp.org/ftp/Specs/archive/23_series/23.501/

Used In Thesis Section(s): Ch.8 Core Networks (MSC→EPC→5GC), Ch.6 Standards (5G), Ch.13 5G-Advanced

Referenced Statement: 5GC is a cloud-native Service-Based Architecture (SBA) with functions such as AMF, SMF, UPF, NRF, PCF communicating via service APIs.

 

[2]

Canonical: 3GPP TS 38.300, "NR; NR and NG‑RAN overall description"

URL: https://www.3gpp.org/ftp/Specs/archive/38_series/38.300/

Used In Thesis Section(s): Ch.6 Standards (5G NR), Ch.8 Core Networks (RAN ↔ Core interfaces)

Referenced Statement: Overall description of 5G NR/NG‑RAN, including architecture, protocol layers, and reference signals.

 

[3]

Canonical: 3GPP TS 36.300, "E‑UTRA and E‑UTRAN; Overall description; Stage 2"

URL: https://www.3gpp.org/ftp/Specs/archive/36_series/36.300/

Used In Thesis Section(s): Ch.5 Topologies (macro/micro/pico), Ch.6 Standards (LTE), Ch.4 Modulation/Access

Referenced Statement: LTE uses OFDMA (downlink) and SC‑FDMA (uplink) and defines key E‑UTRAN procedures.

 

[4]

Canonical: ETSI GSM 05.05 (3GPP TS 45.005), "Radio transmission and reception"

URL: https://www.etsi.org/deliver/etsi_gts/05/0505/05.05.01_60/  |  https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=1448

Used In Thesis Section(s): Ch.2 Timeline (2G), Ch.6 Standards (GSM)

Referenced Statement: GSM carrier spacing is 200 kHz; baseline RF characteristics for GSM.

 

[5]

Canonical: 3GPP HSPA (Release 5/6) — see TS 25.308 / TS 25.306; summary: "High Speed Packet Access (HSDPA/HSUPA)"

URL: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=1411

Used In Thesis Section(s): Ch.2 Timeline (3G), Ch.6 Standards (UMTS/HSPA)

Referenced Statement: HSDPA introduced downlink peak rates on the order of ~14.4 Mb/s with link adaptation and fast scheduling.

 

 

[6]

Canonical: 3GPP TR 38.901, "Study on channel model for frequencies from 0.5 to 100 GHz"

URL: https://www.3gpp.org/ftp/Specs/archive/38_series/38.901/

Used In Thesis Section(s): Ch.3 Propagation & Link Budget, Ch.6 Standards (5G NR), Ch.13 5G‑Advanced

Referenced Statement: NR channel modeling across sub‑6 GHz and mmWave, including path loss, delay spread, and mobility (Doppler) effects.

 

[7]

Canonical: ITU‑R P.525‑4, "Calculation of free-space attenuation"

URL: https://www.itu.int/rec/R-REC-P.525/en

Used In Thesis Section(s): Ch.3 Propagation & Link Budget

Referenced Statement: Free‑space path loss (FSPL) formula used as baseline for link budgets.

 

[8]

Canonical: 3GPP TS 33.501, "Security architecture and procedures for 5G System"

URL: https://www.3gpp.org/ftp/Specs/archive/33_series/33.501/

Used In Thesis Section(s): Ch.9 Security & Authentication, Ch.6 Standards (5G), Ch.8 Core Networks

Referenced Statement: 5G‑AKA, SUCI (concealed SUPI), and mapping of integrity/confidentiality algorithms (e.g., NEA1/2/3, NIA1/2/3).

 

[9]

Canonical: NIST Cybersecurity White Paper — SUCI Overview (supporting 5G privacy)

URL: https://csrc.nist.gov/publications

Used In Thesis Section(s): Ch.9 Security & Authentication

Referenced Statement: Rationale and mechanism for protecting subscriber identifiers via SUCI to mitigate IMSI‑catcher attacks.

 

[10]

Canonical: LoRa Alliance, "LoRaWAN® L2 1.0.4 Specification"

URL: https://lora-alliance.org/resource_hub/lorawan-specification-1-0-4/  |  https://resources.lora-alliance.org/

Used In Thesis Section(s): Ch.7 Specialized Systems (LoRaWAN), Ch.6 Standards (LPWAN)

Referenced Statement: Device classes (A/B/C), MAC behavior, and certification references for LPWAN deployments.

 

 

[11]

Canonical: IEEE 802.11ax (Wi‑Fi 6) standard — overview via vendor technical white papers

URL: https://www.cisco.com/c/en/us/solutions/collateral/enterprise-networks/802-11ax-solution/white-paper-c11-740788.html

Used In Thesis Section(s): Ch.6 Standards (Wi‑Fi), Ch.10 Performance/QoS (dense deployments)

Referenced Statement: OFDMA, BSS Coloring, and Target Wake Time (TWT) as key Wi‑Fi 6 features for efficiency in dense environments.

 

[12]

Canonical: LANCOM Systems Whitepaper (Wi‑Fi 6)

URL: https://www.lancom-systems.com/wi-fi6-whitepaper/

Used In Thesis Section(s): Ch.6 Standards (Wi‑Fi)

Referenced Statement: Practical explanations of 802.11ax mechanisms (OFDMA, BSS Coloring) and deployment guidance.

 

[13]

Canonical: IEEE 802.16e‑2005 (Mobile WiMAX) — mobility/QoS extensions

URL: https://standards.ieee.org/standard/802_16e-2005.html  |  https://ws680.nist.gov/publication/get_pdf.cfm?pub_id=150568

Used In Thesis Section(s): Ch.6 Standards (WiMAX), Ch.2 Timeline (4G contenders)

Referenced Statement: Mobile WiMAX extensions enabling mobility, QoS, and PHY/MAC enhancements; historical 4G context.

 

[14]

Canonical: 3GPP TR 38.821, "Solutions for NR to support non‑terrestrial networks (NTN)"

URL: https://www.3gpp.org/ftp/Specs/archive/38_series/38.821/

Used In Thesis Section(s): Ch.6 Standards (5G/NTN), Ch.7 Specialized Systems (Satellites), Ch.13 Road to 6G

Referenced Statement: Architectural and PHY/MAC adaptations for NR over LEO/HAPS, including timing/Doppler considerations.

 

[15]

Canonical: 3GPP TS 38.211/38.213/38.214 (NR PHY, control, and procedures) — numerology

URL: https://www.3gpp.org/ftp/Specs/archive/38_series/38.211/

Used In Thesis Section(s): Ch.4 Modulation/Coding/Multiple Access, Ch.6 Standards (5G NR)

Referenced Statement: NR subcarrier spacing Δf = 15×2^μ kHz, slot/frame structure, MCS tables and link adaptation context.

 

 

[16]

Canonical: ETSI/3GPP Algorithm specifications (Snow3G, AES‑CTR, ZUC) — mapped via TS 33.501 Annexes

URL: https://www.3gpp.org/ftp/Specs/archive/35_series/  |  https://www.3gpp.org/ftp/Specs/archive/33_series/33.501/

Used In Thesis Section(s): Ch.9 Security & Authentication

Referenced Statement: Families of confidentiality (NEA1/2/3) and integrity (NIA1/2/3) algorithms used in LTE/NR.

 

[17]

Canonical: ITU‑R P.838‑3 (specific attenuation model for rain) and P.840‑8 (rain height/cloud models)

URL: https://www.itu.int/rec/R-REC-P.838/en  |  https://www.itu.int/rec/R-REC-P.840/en

Used In Thesis Section(s): Ch.3 Propagation (mmWave rain fade), Ch.13 5G‑Advanced (FR2 planning)

Referenced Statement: Quantitative rain attenuation models applicable to FR2/mmWave planning.

 

[18]

Canonical: RF Cafe (historical engineering tables) — AMPS 30 kHz channelization

URL: https://www.rfcafe.com/references/electrical/wireless-communications-standards.htm

Used In Thesis Section(s): Ch.2 Timeline (1G), Ch.6 Standards (1G)

Referenced Statement: AMPS channel spacing of 30 kHz and associated band plans (historical reference).

 

[19]

Canonical: 3GPP TS 23.401 (EPC); IMS/VoLTE (TS 24.229); 24/26 series for SIP/codec

URL: https://www.3gpp.org/ftp/Specs/archive/23_series/23.401/  |  https://www.3gpp.org/ftp/Specs/archive/24_series/24.229/  |  https://www.3gpp.org/ftp/Specs/archive/26_series/

Used In Thesis Section(s): Ch.8 Core Networks (LTE/EPC), Ch.10 Performance/QoS

Referenced Statement: EPC split (MME, SGW, PGW) and IMS/VoLTE SIP-call control with codec implications.

 

[20]

Canonical: 3GPP TS 36.212/36.213 (LTE PHY coding & modulation; link adaptation)

URL: https://www.3gpp.org/ftp/Specs/archive/36_series/

Used In Thesis Section(s): Ch.4 Modulation/Coding (4G), Ch.10 Performance/QoS

Referenced Statement: Coding/modulation schemes, HARQ, higher-order QAM, and MCS tables used for LTE/LTE‑A throughput claims.

 

 

[21]

Canonical: 3GPP TS 45.002/45.003 (GSM physical channel, modulation)

URL: https://www.3gpp.org/ftp/Specs/archive/45_series/

Used In Thesis Section(s): Ch.4 Modulation/Coding (2G), Ch.2 Timeline (2G)

Referenced Statement: GMSK modulation, time-slot/multiframe structure for GSM; groundwork for GPRS/EDGE evolution.

 

[22]

Canonical: 3GPP TS 25.331/25.211 (UMTS/W‑CDMA RRC & PHY)

URL: https://www.3gpp.org/ftp/Specs/archive/25_series/

Used In Thesis Section(s): Ch.4 Modulation/Coding (3G), Ch.2 Timeline (3G)

Referenced Statement: W‑CDMA spreading/channelization and RRC procedures; HSPA context.

 

[23]

Canonical: IEEE 802.11be (Wi‑Fi 7) – overview (IEEE/vendor)

URL: https://standards.ieee.org/ieee/802.11be/7421/  |  https://www.intel.com/content/www/us/en/products/docs/wireless/wi-fi-7.html

Used In Thesis Section(s): Ch.6 Standards (Wi‑Fi evolution), Ch.10 Performance/QoS

Referenced Statement: 320‑MHz channels and Multi‑Link Operation (MLO) as Wi‑Fi 7 advancements.

 

[24]

Canonical: LoRa Alliance — Regional Parameters (RP2‑1.0.4)

URL: https://lora-alliance.org/resource_hub/rp2-1-0-4-lorawan-regional-parameters/

Used In Thesis Section(s): Ch.7 Specialized Systems (LoRaWAN), Ch.3 Spectrum (ISM bands)

Referenced Statement: Regional channel plans, duty‑cycle, and EIRP constraints for LoRaWAN.

 

[25]

Canonical: ITU IMT documents (IMT‑Advanced & IMT‑2020 requirements/definitions)

URL: https://www.itu.int/en/ITU-R/terrestrial/im/pages/im.aspx

Used In Thesis Section(s): Ch.2 Timeline (4G/5G definitions), Ch.6 Standards

Referenced Statement: Formal generational requirements and performance frameworks for IMT‑Advanced (4G) and IMT‑2020 (5G).

 

Image Credits

Credits to image owners under Creative Commons or taken in full context

 

 

 

 

Science Museum Group. Motorola StarTAC mobile phone, 1997.. 1997-1650 Science Museum Group Collection Online. Accessed 4 October 2025.

https://collection.sciencemuseumgroup.org.uk/objects/co430373/motorola-startac-mobile-phone-1997.

 

 

 

 

Science Museum Group. Motorola 4800 Analogue Mobile. Y2005.44.1 Science Museum Group Collection Online. Accessed 5 October 2025. https://collection.sciencemuseumgroup.org.uk/objects/co8413432/motorola-4800-analogue-mobile.

 

Science Museum Group. Motorola A1000 mobile phone. 2014-495/1 Science Museum Group Collection Online. Accessed 5 October 2025. https://collection.sciencemuseumgroup.org.uk/objects/co8442766/motorola-a1000-mobile-phone

 

 

 

Science Museum Group. Nokia 9000 Communicator mobile phone. 2014-44 Science Museum Group Collection Online. Accessed 5 October 2025. https://collection.sciencemuseumgroup.org.uk/objects/co8439032/nokia-9000-communicator-mobile-phone

Credit to SAMSUNG (Galaxy A12, taken out from a free-to-distribute)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Apple IPhone17 Pro Max © APPLE.COM

POST GRADUATE PATHWAY

KEY TAKE AWAY IMAGES

 

Abstract & Intro

• Timeline overview (1980s–5G).
• Conceptual diagram: radio systems ecosystem (cellular, Wi-Fi, LPWAN, satellite).
• World map showing ASEAN & ANZ focus.

Chapter 1 – Introduction

• Cellular architecture basics.
• Comparative chart: wireless vs wired.
• Illustration of spectrum evolution.

Chapter 2 – Historical Timeline

• 1G–5G evolution timeline.
• Generational throughput/latency chart.
• Device evolution pictorial (brick phone → smartphone → IoT).

Chapter 3 – Spectrum & Regulation

• 900 MHz propagation graph.
• ASEAN spectrum allocation table (visualized).
• ANZ spectrum licensing diagram.

Chapter 4 – Propagation & Link Budget

• Path loss models (free space vs Hata).
• Link budget block diagram.
• Antenna/MIMO illustration.

Chapter 5 – Network Topologies

• Macro/micro/pico/femto cell hierarchy.
• HetNet diagram.
• Mesh/LPWAN star-of-stars.

Chapter 6 – Standards by Generation

• Table: 1G–5G standards comparison.
• Wi-Fi evolution chart.
• LoRaWAN architecture.

Chapter 7 – Specialized Systems

• TETRA ATEX rugged radio pictorial.
• ST GRID hybrid phone diagram.
• Satellite constellations map (Beidou, Starlink, Inmarsat).

Chapter 8 – Core Networks

• MSC → EPC → 5GC evolution.
• Service-based architecture diagram.
• NFV/SDN cloud-native illustration.

Chapter 9 – Security & Authentication

• Authentication methods evolution (1G–5G).
• Attack surface diagram (IMSI catcher, rogue base station).
• AI-based intrusion detection pipeline.

Chapter 10 – Performance & QoS

• KPI dashboard mockup.
• QCI/5QI mapping table.
• Throughput/latency scatterplot.

Chapter 11 – Deployment & Testing

• Drive test vs crowdsourced testing diagram.
• Digital twin illustration.
• Optimization workflow (AI loop).

Chapter 12 – Case Studies

• ASEAN adoption map.
• ANZ rural broadband coverage.
• Comparative chart: LTE vs Starlink vs LoRaWAN.

Chapter 13 – 5G Advanced & 6G

• RIS (reconfigurable intelligent surfaces) pictorial.
• Sub-THz spectrum chart.
• 6G vision roadmap (timeline).

Chapter 14 – Conclusion

• Pyramid diagram (1G foundation → 6G cognitive fabric).
• ASEAN vs ANZ summary chart.
• Future integration flowchart (AI + satellite + terrestrial).