Hot Topics in Mobile Communications
· This course presents some recent or emerging HOT TOPICS within the area of mobile networks.
· The course is modified from 2014 (and earlier) versions to allow focus on updated set of hot topics and trends in mobile communications.
· We emphasize emerging techniques to be used in future 5G mobile networks to allow for a significant increase in user quality, and network capacity.
· The course earns 5 ECTS
· We cover hot topics for 5G such as "Massive MIMO" , "network cooperation", "interference management", and "device coordination". These topics cover 21 hours.
· In the other 21hours, external experts (Intel, Huawei, ETSI, etc.) from Industry reveal hot topics seen from the wireless networking industry.
Teaching and Learning Methods : Lectures, Exercise and Lab sessions (group of 2 students)
Course Policies : Attendance to Lab session is mandatory (25% of final grade).
This course presents the architecture of microprocessor-based systems, from the internals of the processors themselves to the main peripherals that surround them and make a complete computing machine, capable of running operating systems like GNU/Linux, Android, Windows, iOS...
Teaching and Learning Methods : Lectures, team-work, lab sessions, mini-conferences by industrials
Course Policies : Attendance to the lab sessions and the mini-conferences is mandatory
Digital communications is the study of physical layer transmission and reception strategies in communications equipment such as mobile communication devices, high-speed ethernet, optical communications and subscriber-line communications. It comprises the areas of (i) statistical modelling of communication channels (ii) design of coding and modulation systems for error resiliency (iii) design of demodulation and decoding strategies and the associated methods for assessing their performance. In addition to providing an overview of communication channels and classical modulation strategies, this course takes a hands-on approach by focusing on the details of a few critical elements of digital communications pertaining to modern wireless transceivers, both coherent and non-coherent.
Teaching and Learning Methods :: Lectures and hands-on lab sessions in MATLAB using signals acquired from local 4G networks.
Course Policies : Attendance to lab sessions is mandatory.
This course aims to present a treatment of mathematical methods suitable for engineering students who are interested in the rapidly advancing areas of signal analysis, processing, filtering and estimation. Significant current applications relate to, e.g., speech and audio, music, wired and wireless communications, instrumentation, multimedia, radar, sonar, control, biomedicine, transport and navigation. The course presents a study of linear algebra, probability, random variables, and analogue systems as a pre-requisite to material relating to sampled-data systems. Time permitting, the final part of the course covers the concepts of random processes, the analysis of random signals, correlation and spectral density.
Teaching and Learning Methods: The course is comprised of lectures, exercises and laboratory sessions.
Course policies: This course is aimed at students who have NOT already completed preparatory classes. Completion of all in-lecture examples is strongly advised.
The goal of MOBCOM is to provide a fundamental understanding of mobile communication systems. The course will seek to describe the key aspects of channel characteristics/modeling, of communication techniques, and to describe the application of these techniques in wireless communication systems.
Teaching and Learning Methods : Lectures and Lab sessions (group of 2-3 students)
Course Policies : Attendance to Lab session is mandatory.
This course presents the three main mobile platforms and their ecosystems, namely Android, iOS, and PhoneGap/WebOS. It explores emerging technologies and tools used to design and implement feature-rich mobile applications for smartphones and tablets taking into account both the technical constraints relative to storage capacity, processing capacity, display screen, communication interfaces, and the user interface, context and profile.
Teaching and Learning Methods : Lectures, Lab sessions (group of 2 students), and a challenge project ( group of up 2 4 students).
Course Policies : Attendance to Lab session is mandatory.
This course presents a series of mobile systems in their entirety to synthetize the knowledge gained in more fundamental courses. It explores current and emerging standards and follows the evolution of various mobile services.
Teaching and Learning Methods : Lectures and Lab sessions (group of 2 students)
Course Policies : Attendance to Lab session is mandatory.
Would you like to investigate beyond the surface of Windows, MacOS, Linux, Android? Fed up with not understanding the origin of segmentation faults, why you need to eject a USB key before physically removing it, or why/how your Android system can execute Pokemon Go and Facebook at the same time? You want to delve into the details of the inner workings of the Linux kernel? Join us to discover the power of Operating Systems!
Teaching and Learning Methods : Lectures (40%), Lab sessions (40%), Project (20%)
Course Policies: Attendance to some lab and project session is mandatory
This course covers a variety of topics, all related to the use and management of a Linux operating system. In particular, the course is divided in three parts dedicated respectively to the command-line, to the Python programming language, and to maintaining, compiling, and installing applications.
« Those who fail to plan, plan to fail... ». Architects, tailors, and directors all use plans (or models) for their creation, and software engineers are no exception. Thus, it is a common practice for software project managers to rely on the UML langage to document their software projects, and to perform modeling of the software itself.
Teaching and Learning Methods : Lectures (20%), Exercises (40%), Lab sessions (40%)
Course Policies: Attendance to labs is mandatory
The proper treatment of modern communication systems requires the modelling of signals as random processes. Often the signal description will involve a number of parameters such as carrier frequency, timing, channel impulse response, noise variance, interference spectrum. The values of these parameters are unknown and need to be estimated for the receiver to be able to proceed.
Parameters may also occur in the description of other random analysis of communication networks, or in the descriptions of sounds and images, or other data, e.g. geolocation. This course provides an introduction to the basic techniques for estimation of a finite set of parameters, of a signal spectrum or of one complete signal on the basis of a correlated signal (optimal filtering, Wiener and Kalman filtering). The techniques introduced in this course have a proven track record of many decades. They are complementary to the techniques introduced in the EURECOM course Stat. They are useful for other application branches such as machine learning, in the EURECOM courses MALIS and ASI.
Teaching and Learning Methods: Lectures, Homework, Exercise and Lab session (groups of 1-2 students depending on size of class).
Course Policies: Attendance of Lab session is mandatory (15% of final grade).
This course provides a broad introduction to cryptography and communication security mechanisms based on cryptography. The course covers fundamental aspects such as security evaluation criteria and the mathematical constructs underlying cryptographic primitives as well as applied aspects like the design of major encryption and hashing algorithms, details of security mechanisms relying on cryptography such as data encryption, integrity, digital signature, authentication, key management, and public-key infrastructures.
Teaching and Learning Methods : Lectures and Lab sessions
Course Policies : Attendance to Lab sessions is mandatory.
Optimization theory (convex, non-convex, discrete) is an important field that has application to almost every technical and non-technical field, including wireless communication, networking, machine learning, security, transportation systems, finance (portfolio management) and operation research (supply chain, inventory). It has recently attracted even more interest, as the underlying tool for advanced machine learning techniques for big data (deep neural networks, stochastic gradient descent). In this course we will cover both the fundamentals (algorithms, duality, convergence, optimality) as well as a range of applications in the area of (network) resource allocation, distributed optimization, and machine learning. Special emphasis will be devoted to exemplify applications of optimization techniques to modern engineering and CS problems with the objective of developing skills and background necessary to recognize, formulate, and solve optimization problems.
Teaching and Learning Methods : Lectures supported by exercise sessions, and homework assignments including both problem solving and programming of learned methods (CVX, matlab, python)
Course Policies : Attendance to lectures and exercise session is not mandatory but highly recommended.
The goal of this course is to provide a comprehensive view on recent topics and trends in distributed systems and cloud computing. We will discuss the software techniques employed to construct and program reliable, highly-scalable systems. We will also cover architecture design of modern datacenters and virtualization techniques that constitute a central topic of the cloud computing paradigm. The course is complemented by a number of lab sessions to get hands-on experience with Hadoop and the design of scalable algorithms with MapReduce.
Teaching and Learning Methods: Lectures and Lab sessions (group of 2 students)
Course Policies: Attendance to Lab session is mandatory.
This course provides an introduction to practical security concepts. The goal is to understand common attacks and countermeasures in a range of topics. The course is practice oriented, it describes real attacks and countermeasures. Students will practice attacks on a dedicated server (similar to a Capture the Flag competition).
Teaching and Learning Methods :Weekly class. Some guest lectures. Homework are online challenges, on a number of topics related to the class. A first lab is organized during lecture time to bootstrap challenges.
Course Policies :Class attendance is not checked but generally required to succeed.
The objective of this course is to give students a solid background in Machine Learning (ML) techniques. ML techniques are used to build efficient models for problems for which an optimal solution is unknown. This course will introduce the basic theories of Machine Learning, together with the most common families of classifiers and predictors. It will identify the basic ideas underlying the mechanism of learning, and will specify the practical problems that are encountered when applying these techniques, optimization, overfitting, validation, together with possible solutions to manage those difficulties.
Teaching and Learning Methods : Lectures and Lab sessions (groups of 1 or 2 students)
Course Policies : Attendance to Lab sessions is mandatory
Because multimedia data (in particular image and video) require efficient compression techniques in order to be stored and delivered, image and video compression is a crucial element of an effective communication system.
This course covers the most popular lossless and lossy formats, introduces the key techniques used in source coding, as well as appropriate objective/subjective metrics for visual quality evaluation.
Teaching and Learning Methods: Each class includes a problem session for students to practice the material learned. This course includes a limited number of lab session hours.
Course Policies: It is mandatory to attend lab. sessions.
The course aims at providing students with a basic knowledge and practice about the use of computer algorithms to perform image processing on digital images. The two main objectives attached to Digital Image Processing (DIP) are to improve the visual quality of images and to automatically extract semantic information from visual data (e.g. object recognition).
Teaching and Learning Methods: Each session is split into two parts: 1.5-hour lecture and 1.5-hour lab.
Course Policies: It is mandatory to attend lab. sessions.