M2 Communication and data engineering

1. Course "Digital communications" or equivalent

2. Course “Electronics systems for telecoms" or equivalent.

• GSM
• GPRS
• EDGE
• UMTS
• Wifi (802.11)

Understand all wireless technologies (GSM/ GPRS/ EDGE/ 3G/ Wifi)

Ajay R. Mishra, Fundamentals of Network Planning and Optimisation 2G/3G/4G: Evolution to 5G, Wiley, 2018.

1. Basic knowledge on "Operating systems","Computer Network" and " "Relational database management system"
2. Programming language (Java/C ++)

Part1: Cloud computing:

• Introduction
• Principle of cloud computing
• Fog and edge computing
• Future of cloud (serverless computing)

Part2: IoT

• Introduction to IoT: vision and trend
• Architecture and challenges
• Technology Solution for IoT End Devices
• IoT Edge Devices/Gateways
• Proximity Communications Between Objects and Edges: ZigBee, Thread, BT LE
• Long Range Communication with IoT gateways: LoRa, SigFox, NB-IoT
• End to End Data Gathering: Publish-Subscribe Model: MQTT, AMQP, Stromp
• Open IoT platforms and open initiatives 
• Security and Privacy issues in IoT (energy, commerce, industry, transportation, healthcare, ...) 
• Research activities in IoT (scalability, rate, energy, data mining, data analytics, cost).

Gaining a deep understanding of the software implementation concepts of a cloud so that they can be users - expert developers but also administrators or contributors for such infrastructures. Second part about IoTs, offers students a complete and detailed landscape on the different protocols and technologies that will be used to realize the new system in which we are going to evolve.

1. Perry Lea, Internet of Things for Architects, Packt Publishing Ltd, 2018
2. David Hanes, Gonzalo Salgueiro, Patrick Grossetete, Robert Barton, Jerome Henry, IoT fundamentals, Cisco Press, 2017
3. Nayan B. Ruparelia, Cloud computing, MIT Press, 2016
4. Michael J. Kavis, Architecting the cloud, John Wiley & Sons, 2014
5. Kevin Jackson, Cody Bunch, Egle Sigler, James Denton, OpenStack Cloud Computing Cookbook, Packt Publishing Ltd, 2018

Algorithms, Programming (Python/C/Java)

• Introduction (basic ML and data science)
• Data mining algorithms
• Applications of data mining
• Data stream mining
• Reinforcement Learning, Multi-Armed Bandits
• Graphical Models/Bayesian Networks
• Gaussian Processes

Focusing on the algorithms involved in data-related tasks, collectively grouped under the concept of “data mining”.

• J. Leskovec, A. Rajaraman, J. Ullman. Mining of Massive Datasets, Cambridge University Press, 2014
• S. Abiteboul, I. Manolescu, P. Rigaux, M.-C. Rousset, P. Senellart. Web Data Management, Cambridge University Press, 2012
• R. Sutton, A. Barto. Reinforcement Learning, MIT Press, 2017
• T. Lattimore, C. Szepesvári. Bandit Algorithms, Cambridge University Press, 2020
• C.A. Rasmussen, C. Williams. Gaussian Processes for Machine Learning., MIT press, 2005
• D. Koller, N. Friedman. Probabilistic Graphical Models. MIT press, 2009

• Baseband Transmission
• Single-Carrier Transmission
• Multicarrier Transmission
• Spread Spectrum Signaling
• Fading Channels and Diversity

Knowing how to design a digital communication chain, estimating the performance in function of the technique and the various parameters in order to reach an objective under various constraints.

• S. Benedetto, E. Biglieri, Principles of Digital Transmission with Wireless Applications, Kluwer Academic Plenum Publishers, 1999
• J. Proakis, Digital Communications, McGraw-Hill, 2000
• S. Haykin, Communication Systems, Wiley, 2002
• D. Tse, P. Viswanath, Fundamentals of Wireless Communications, Cambridge University Press, 2005

• Introduction to the Internet
• IPv4, IPv6, NAT, subnetting
• Advanced TCP
• TCP and congestion control. Other transport protocols: CUBIC, SCTP, MPTCP, TCP Compound
• HTTP2.0, HTTP3.0, Multicast
• Quality of service
• Standard applications
• Multimedia Applications and Over the Top

Acquiring an understanding of the basic mechanisms and protocols implemented in telecommunications networks.

• J. F. Kurose and K. W. Ross, “Computer Networking, A Top-Down Approach Featuring the Internet”, Addison Welsey, 2013
• Tanenbaum, “Computer Networks”, Prentice Hall, 2010
• William Stallings, _“Data & Computer Communications_”, 2006
• Peter L Dordal, “An Introduction to Computer Networks”, Loyola University Chicago, edition 2.0.1. online. 
• Olivier Bonaventure, « Computer Networking : Principles, Protocols and Practice », Ed. 2019. Online.
• W. R. Stevens, TCP/IP Illustrated, protocols, Addison Wesley, 2011

Understanding current research activities in the domains of wireless communications and networking, 5G and 6G systems, applications of machine learning, as well as other research topics... The concept of sustainable development will be included. The second objective is to train student how to search for relevant information, how to read and summarize scientific papers.

1. Mastering of communication protocols (TCP-IP, ICMP, ARP)
2. Basic probability theory and discrete mathematics (knowledge of basic abstract algebra and number theory is recommended, but not formally required)
3. Beginner level Python+ SQL

• Methods and algorithms of modern cryptography.
• Network encryption and authentication protocols on various OSI layers.
• Practical and infrastructural aspects of network security.

Providing a solid understanding of modern cryptography, and its application in network protocols.

• Keith M. Martin, Everyday cryptography, Oxford University Press, 2012
• Al Sweigart, Cracking Codes with Python, No Starch Press, 2018.
• Bryan Sullivan and Vincent Liu, Web Application Security, McGraw-Hill Osborne Media, 2011
• John R. Vacca (ed), Computer and Information Security Handbook, Morgan Kaufmann, 2017
• Michael Schwartz and Maciej Machulak, Securing the Perimeter, Apress, 2018
• Evan Gilman and Doug Barth, Zero Trust Networks, O'Reilly, 2017

Sound knowledge in Probability, Random processes, and Linear algebra

• Introduction to error-correcting codes; TD1 on error-correcting codes • Linear cyclic codes; TD2 on Algebraic decoding • Linear convolutional codes • Performance of linear codes under MLD • Factor graphs and the sum-product algorithm • Sparse-graph codes : LDPC codes ; TD3 on LDPC codes • Sparse-graph codes: Density evolution, Code design optimization under iterative decoding; TD4 on turbo codes • Sparse-graph codes: Ensemble enumerators, Code design optimization under MLD • Source coding; TD5 on source coding

The first objective is to understand the basics of source coding (without memory, with memory, …). The second is to understand the basics of algebraic coding (linear codes, polynomial, convolutional, cyclic, BCH, Reed-Solomon, ...) on channels with binary inputs without memory.

In the first part of the course, we remind students of the basics of the algebraic coding theory for conventional binary-input output-symmetric memoryless channels. The second part of the course is devoted to sparse graph codes. We review in detail code construction aspects, iterative decoding, and mathematical tools for design optimization. We then expound the principles of non-binary coding for the Gaussian channel and show how and why coded modulations can also benefit from sparse-graph codes optimized for binary-input channels and iterative decoding.

• R.G. Gallager, Information Theory and Reliable Communications, John Wiley, 1968. • T. Cover, Elements of Information Theory, John Wiley, 1991. • F.J. MacWilliams, N.J.A. Sloane, Theory of Error-Correcting Codes, North Holland Publishing, 1977. • R.J. McEliece, Finite Fields for Computer Scientists and Engineers, Kluwer Academic Publishers, 1987. • W.E. Ryan, S. Lin, Channel Codes, Cambridge University press, 2009 • A.J. Viterbi, J.K. Omura, Principles of Digital Communications and Coding, McGraw Hill, 1979 • K. Sayood, Introduction to data compression, Morgan Kaufmann, 2012

1. Course "Information theory" or equivalent
2. Course "Probability " or equivalent

Supervised learning (regression, classification)
Unsupervised learning (clustering, dimensionality reduction)
Introduction to Neural Networks
Advanced Neural Networks
Variations on auto-encoders and probabilistic Graphical Models
Modern architectural variations for communications and IoT data analytics

This course provides knowledge of advanced machine learning, deep learning and using such techniques in real application using IoT data.

The first part of the course is carried out from practical work intended to learn how to apply machine learning algorithms and statistical pattern recognition on real data. The practical know-how necessary to train and evaluate model performances are teach through examples of implementation on real data.
The second part is on the principles of machine learning in general and deep learning in particular. We will explore both the fundamentals advances in the area of deep learning and the recent applications to the field of IoT and in general communications. Our focus will be on recent applications of deep learning to perform data analytics on the Internet of Things (IoT) communications, including neural networks, auto-encoders, convolutional neural networks and recurrent networks. We will also consider well-known probabilistic graphical models, including undirected models and directed models that have recently shown promise (e.g. Boltzmann machines, Deep Belief Nets).

• Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016
• Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
• Tom M. Mitchell, Machine Learning, McGraw-Hill Education, 1997
• Li Deng and Dong Yu, Deep Learning - Methods and Applications, Now publishers, 2014
• Christopher M. Bishop, Pattern recognition and machine learning, Springer, 2006
• Simon Haykin, Neural Networks and Leaning Machines, Pearson, 2009