Code
IGFF NET 4252
Level
M1
Graduate
Graduate
Semester
Fall
Domain
Réseaux
Program
Programme Ingénieur
Language
Anglais/English
ECTS Credits
2,5
Class hours
25
Workload
40
Program Manager(s)
Department
- Réseaux et Services Multimédia Mobiles
Organisation
Cours/TD/TP/projet/examen : 6H/6H/6H/6H/1HCDIO Skills
- 1.1 - Knowledge of basic science including mathematics and others
- 1.2 - Core engineering fundamental knowledge and other disciplines
- 1.3 - Advanced engineering fundamental knowledge, methods and tools
- 2.1 - Analytical reasoning and problem solving
- 2.2 - Experimentation, investigation and knowledge discovery
- 2.2.1 - Hypothesis Formulation
- 2.2.2 - Survey of Print and Electronic Literature
- 2.2.3 - Experimental Inquiry
- 3.2.3 - Written Communication
- 3.2.6 - Oral Presentations
- 4.1.3 - Society's Regulation of Engineering
- 4.1.4 - The Historical and Cultural Context
- 4.1.5 - Contemporary Issues and Values
Keywords
Radio Mobile Transmisssion & Networks, IoT and AI, AI for Networks, Multimodal AI, Open-Source AI for Networks and Services, Networks for AI, 5G & 6G key technologies, 5G/6G and AI
Content
Mobile Radio Transmission and Networks
Applied and pratical AI basic introduction (from the applied and pratical point of view in order to accomplish the two projects of the course)
IoT and IA
IA for Networks & Services
Multimodal IA for Networks & Services
Open-Source IA for Networks & Services
5G/6G key technologies
5G/6G and IA
Networks & Services for IA
Evaluation
grade over 20
Assessment formula
The evaluation will be based on in-class tests (30%) and a research project (70%).
Pedagogical methods
This course is divided into two parts: NET 4252 (P1-1) and NET 4254 (P1-2).
This course includes two pratical supervised and guided projects of AI for networks and services.
In the first project, the students will study a use case of AI in network optimization: Q-Learning applied to the control of the transmit power in a LoRa network.
They will simulate an IoT network (LoRa network) in Python and compute its Energy Efficiency (EE) with a communication model.
They will learn how reinforcement learning works (state, action, reward, policy), how Q-Learning improves a table of action values by trial and error, and how exploration and hyperparameters affect whether learning converges.
Finally, they will learn to validate a method by comparing it with a reference and judging the result by its trade-offs.
In the second project, an introduction to Machine Learning and Multimodal AI will be given: How a Machine Learns to See, and How Multimodal AI can connect the gap between images and language. Then, the students will explore the open-source ecosystem, then independently build a video surveillance service powered by a vision-language model.
The students will understand how an intelligent video surveillance service works, and they will learn how to build one using an open-source AI model.
By the end of this second project, the students will be able to:
• Explain what a Machine Learning model is, how it learns, and how to measure its quality.
• Understand how a multimodal model connects images, videos, and text.
• Select, deploy, and utilize an open-source model (Qwen) within a video surveillance service.
• Evaluate an AI service based on concrete criteria: false alarms, latency, cost, and compliance.