Explore the programs and courses offered by Signals and Telecommunication Systems (Doctorate)
Browse Programs Admission InformationResearch and training are organized around four main interrelated and complementary themes. The four research teams are:
1. Detection and Estimation
2. Optimization of Detection Systems
3. Communication Systems
4. Architecture and Implementation of Systems
1. Detection and Estimation
Adaptive CFAR (Constant False Alarm Rate) detection is the main tool used to study and
analyze mid-level detectors, including those based on order statistics and maximum likelihood. Specifically, the goal is to develop mathematical models of clutter and targets that characterize non-Gaussian environments (Weibull, Log-normal, and K distributions). 2. Estimating parameters of non-Gaussian clutter involves developing estimation methods with the primary aim of improving the performance of adaptive detection.
2. Optimization of Detection Systems
This theme covers all methods and concepts related to the calculation of optimal thresholds. These methods incorporate tools such as Genetic Algorithms (GAs) with various evolution strategies, neural networks, fuzzy logic, and neuro-fuzzy networks. The goal is to propose new detectors and to develop detection algorithms. The second phase involves implementing these algorithms in real-time for integration into the detection chain.
3. Communication Systems
This research aims to develop several lines of investigation in the broad field where computer science and telecommunications converge. Mobile telephony is a prime example. We are working on spread-spectrum multiple access techniques, specifically adaptive detection of PN (Pseudo Noise) codes in DS-CDMA (Direct Sequence Code Division Multiple Access) systems, which are relevant to next-generation communication systems. It is also of interest to develop new algorithms for data compression and encryption, especially in light of the explosion and abundance of communication technologies in everyday life, such as mobile phones, the Internet, digital television, and banking transactions. In this context, reducing the amount of information involved and securing it becomes essential.
4. Architecture and Implementation of Detection Systems
The implementation of detection algorithms involves translating mathematical models into computational schemes. Regardless of the application (e.g., radar or spread-spectrum communication systems), the main objective of this research area is to develop operational architectures that are synthesizable and function in real-time.
To synthesize these structures at the RTL (Register Transfer Level), various conceptual approaches should be explored.
1. Knowledge Reinforcement Program
Module 1: Digital Techniques and Matlab
Module 2: Advanced Telecommunications Techniques
Module 3: Radar Technologies
Module 4: Modeling, Estimation, and Distributed Binary and Fuzzy CFAR Detection – Digital Design Flow
2. Technical English Module
Module 1: Terminology and Speeches
Module 2: Practical English
3. Seminars
Seminar 1: Compressed Sensing
Seminar 2: Cryptography
Seminar 3: Artificial Intelligence for Radar Signal Detection and Estimation
Objectives of Scientific Research, Technological Development, and Integration of Artificial Intelligence.Beyond its educational role in training students through research, and its ongoing activities focused on developing techniques for optimization, detection, estimation, and implementation in communication systems, the program has set a primary goal: to become a center for simulation, experimentation, and development that integrates modern communication tools and advances in artificial intelligence. The program aims to bring together, on a single site, all the necessary software and hardware components to address the various aspects of communication technologies while integrating AI as a lever for performance and innovation. It will pursue four main objectives:
1. Learning and mastering emerging technologies, including artificial intelligence, by our
students (PhD candidates, Bachelor's, and Master's students) to effectively prepare them
for the world of research and the job market.
2. Developing educational tools enriched by practical AI applications, making learning more
interactive and adapted to current technological demands.
3. Improving scientific output focused on deepening knowledge in detection and estimation
systems (Radars), by integrating artificial intelligence to enhance the accuracy,
performance, and adaptability of these systems. The implementation of solutions on
specialized circuits will also include AI architectures. Research results will be
disseminated through publications, scientific articles, and presentations at national and
international conferences.
4. Creating a research and development space serving the industry, aiming to solve complex
problems in communication systems using AI. Services, studies, and partnerships will be
offered to strengthen the financial autonomy of the laboratory and promote technology
transfer.