| Course Name | Mobile Network Technologies: 5g and Beyond |
| Course Code | 25WN613 |
| Program | M.Tech. Wireless Networks & Applications (Specialising in IoT, AI, 5G, Blockchain) (For Working Professionals & Regular Students) |
| Semester | 2 |
| Credits | 4 |
| Campus | Amritapuri |
Foundation – 4G Network architecture
Overall core architecture- Access Stratum and Non-Access Stratum- End to End Security Overview-Radio access network -Physical layer & protocols – Key Network and UE procedures: – Call set-up/release, Mobility management in idle mode and active mode (handover)
Specialist – 4G/LTE/LTE-A, Small Cells
A deeper understanding of the concept of the platform via the case study approach. Relationship with WNA industry and applicability of platforms in the today’s industrial context. Building blocks of the platforms in terms of notions of Application Programming Interfaces (APIs) and usage scenarios for APIs.
Convergence foundations and introduction to Cognitive networks
Unlicensed spectrum- Private Networks-Neutral Hosts-Wi-Fi Technology Evolution-Introductory concepts-LTE in Wi-Fi- Software Defined Radios – Concepts of 4G, 5G private networks and insights into Neutral host networks. Trends in cognitive networks and realisation of cognitive networks through AI/ML.
5G
Introduction to 5G (Network of networks) covering devices, systems, things/machines and big data. Overview of key technology enablers in 5G architecture, System Design and framework for the 5G edge supporting massive IoT devices/Cyber Physical Systems. Introduction to candidate proposals in 5G, Software Defined Networking (SDN)-Performance Management. Key advancements in Radio Access Networks (RAN) –5G Open RAN Architecture, RAN (RIC, xApps/rApps/dApps). Introduction to framework of driving testing and log analysis, network diagnostics. Case studies of 5G usage scenarios including key design elements (protocol stacks, latency, and high data rate) to support industry verticals like (Health Care, Enterprise, Driverless Cars and Smart Cities).
AI Native Networks
Radio technology induced cognition -application of AI in the physical layer, intelligent beam prediction, RAN slicing framework using AI utilising RAN Data Analytics Function (RAN-DAF) and Service Management and Orchestration (SMO). Introduction to the role of Large Language Models (LLMs) in networks, AI/ML Workflow Process for the O-RAN Architecture. Energy efficiency in O-RAN, sleeping modes and cell switched on-off, antenna selection, dynamic spectrum management and traffic steering.
Course Outcome Statement (CO)
| CO1 | Appreciation of the legacy mobile network technologies providing background to the advances in the Wireless technologies |
| CO2 | Appreciation of convergence in wireless technologies |
| CO3 | Specialist knowledge in 4G networks. Understanding of the key performance improvements compared to 3G and learn about small cells |
| CO4 | Raise the 5G awareness and prepare for focussed R&D at 5G specialist or PhD level |
| CO5 | Ability to work in industrial environment in supporting and service management areas |
| CO6 | Prepare with skill set required in service provider environment and network benchmarking skills |
CO – PO Affinity Map
| PO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PS01 | PSO2 | PSO3 |
| CO | |||||||||||||||
| CO1 | 3 | 3 | 2 | 3 | 1 | 2 | 1 | 1 | 3 | 3 | 2 | 3 | 3 | 3 | 3 |
| CO2 | 3 | 3 | 3 | 3 | 2 | 2 | 1 | 1 | 3 | 3 | 2 | 3 | 3 | 3 | 3 |
| CO3 | 3 | 3 | 3 | 3 | 2 | 2 | 1 | 1 | 3 | 3 | 2 | 3 | 3 | 3 | 3 |
| CO4 | 3 | 3 | 2 | 3 | 3 | 2 | 1 | 2 | 3 | 3 | 2 | 3 | 3 | 3 | 3 |
| CO5 | 3 | 2 | 3 | 3 | 2 | 2 | 1 | 3 | 3 | 3 | 2 | 3 | 3 | 3 | 3 |
| CO6 | 3 | 3 | 3 | 3 | 2 | 2 | 1 | 3 | 3 | 3 | 2 | 3 | 3 | 3 | 3 |
3-strong, 2-moderate, 1-weak
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