The Master of Science in Signal Theory and Communications (MSTC) is a programme offered by the Signals, Systems and Radiocommunications (SSR) Department of the Higher Technical School of Telecommunication Engineers (ETSIT) of the Polytechnic University of Madrid (UPM), and provides a complementary perspective with respect to other professional Masters from ETSIT-UPM. Its aim is to equip highly motivated students with up-to-date skills in some of the hottest topics demanded worldwide by industries, research centres and academia. The programme is offered fully in English: lectures, study material, and interaction with professors.

MSTC has two tracks:

  • Radiofrequency technologies and systems: corresponds to a classic telecommunication engineering area led by SSR.
  • Signal processing and machine learning for big data: addresses new challenges in applying signal processing and machine learning techniques to massive datasets, covering topics that are currently in high demand in different industry sectors.
  • Accreditations

    2016 and 2021

  • Language

    English

  • Places offered

    40

  • Total credits

    60 ECTS

  • Min.-max. credits per year

    24-37 ECTS part time
    38-60 ECTS full time

  • Modality

    Face-to-face

“Radiofrequency technologies and systems” track

The “RadioFrequency (RF) technologies and systems” track is structured in nine specific subjects, other than the two common ones shared with the other track. Of these nine specific subjects, a block is devoted to fundamentals and technologies, and another to RF communication. The first block covers RF and antenna technologies, understanding and use of modern electromagnetic solvers, software radio, and advanced measurement techniques. The second block covers mobile communication systems, MIMO systems, and secure RF communications. The integration of technologies and applications offers students a new and fresh outlook of modern RF systems.

Optimization fundamentals

3 ECTS

Description

This course covers the fundamentals of the optimization of functions of continuous variables, considering both analytical and algoritmic aspects. Emphasis is made on techniques based on Lagrange and Karush-Kuhn-Tucker multipliers for constrained optimization. All the topics are motivated with concrete problems derived from practical applications.

Radiofrequency optimization techniques

3 ECTS

Description

The design of radiofrequency circuits and subsystems by means of electromagnetic and circuit simulators requires solving global optimization problems, often of noisy and costly functions. We will review the better-known heuristic methods, such as simulated annealing, genetic algorithms and evolution strategies. The use of surrogate models will be also dealt with by reviewing the space mapping technique. All these topics will be illustrated by practical projects, where students will optimize different radiofrequency subsystems or components.

Radiofrequency technologies

6 ECTS

Description

This course is a comprehensive approach to Radio Frequency systems and the technologies involved. It is not a design course for active and passive components or circuits. It is a top-down approach that begins with the specifications of a system and finishes with the selection of the appropriate technology, components, and circuits to meet these specifications. An in-depth understanding of the variety of components in the market, their specifications and limitations is basic for a successful design of complex RF systems.

Advanced topics on antenna technologies

6 ECTS

Description

In this course a solid formation on advanced concepts in antennas technology is given. It covers both theoretical and practical aspects. Commercial or own software will be used for the design and simulation of antennas. The course is based on PBL concept.

Mobile communications: 4G and beyond

3 ECTS

Description

In this course are presented the fundamentals of modern mobile communications and the planning of 4G systems. The course is focused in RF interface. Several techniques of RF resources management are given. Finally an overview of the future 5G systems is presented.

Design of communication systems and equipment

6 ECTS

Description

In this course it is described in deep the elements needed, hardware and software, for the implementation of digital communication systems. Digital communications fundamentals are given as well the advanced concepts on digital signal processing to correct practical problems in the transmission channel.

From array processing to MIMO communications

6 ECTS

Description

We present a unified view of array processing, modern beam forming and MIMO systems including Array processing. Adaptive antennas. Implementation issues related to MIMO communications, Single user MIMO communications, Multiple user MIMO communications, Multiple user / Multiple cells MIMO communications, Massive MIMO, Virtual MIMO and wireless sensor networks.

Secure radiofrequency communications

3 ECTS

Description

Este curso se centra en los sistemas de comunicación que pueden ser atacados en un entorno hostil. Medidas electrónicas y contramedidas para las comunicaciones. Los primeros problemas son los métodos de transmisión robustos, y luego se extienden a técnicas resistentes a ataques inteligentes.

Laboratory course on radiofrequency measurements

6 ECTS

Description

A number of practical sessions will be carried out on antenna measurement, materials characterization, calibration of vector network analyzers, active components measurement, Electromagnetic compatibility etc.

Computational electromagnetics

6 ECTS

Description

It will present the fundamentals of the design and analysis of circuits and passive circuits at RF and microwaves frequency using the most used numerical method, MoM, FDTD, FEM, GTD, Mode Matching etc. Advanced design will be carried out by using commercial software available.

Master's Thesis (MT)

12 ECTS

Description

The Final Master Thesis offers a great opportunity to closely collaborate with your favorite professor on advanced topics to be agreed with him/her. Located in the second semester with 12 ECTS, at that time you will have the global perspective about which are the more challenging topics or more demanded by local or international companies you desire to deepen. After an initial discussion with your academic advisor about your preferences, he/she will put in contact with the most suitable researcher among our professors who will lead your final stage before obtaining the final degree. You will have the chance to access to the professional laboratories in our department and get involved in our research or our technological transfer projects. Finally you will have to elaborate an extended report describing your achievements and make an oral presentation defending your results in front of other professors experts in the field. In the end, you will have gained full professional maturity to continue your career in academia or high technological companies with unique capabilities.

“Signal processing and machine learning for big data” track

The “signal processing and machine learning for Big Data (BD)” track is structured in twelve specific subjects, other than the two common ones shared with the other track. Fundamental subjects on optimization methods for BD, statistical modelling, and time series analysis, provide the background for other subjects devoted to machine learning subjects (predictive and descriptive learning, reinforcement learning and bio-inspired models), and signal processing techniques for BD. The track programme has been designed to provide the scientific foundations and practical orientation towards data analytics and artificial intelligenge in different industrial sectors, including telecommunications and media.

Optimization fundamentals

3 ECTS

Description

This course covers the fundamentals of the optimization of functions of continuous variables, considering both analytical and algoritmic aspects. Emphasis is made on techniques based on Lagrange and Karush-Kuhn-Tucker multipliers for constrained optimization. All the topics are motivated with concrete problems derived from practical applications.

Optimization techniques for big data analysis

3 ECTS

Description

Esta asignatura utiliza la mayoría de los temas ya proporcionados en Fundamentos de optimización, ahora enfocados al problema específico que surge con datos masivos. Aunque proporcionaremos los fundamentos teóricos sobre las técnicas evolucionadas, enfatizaremos diferentes estudios de casos clave en aplicaciones de big data. Distinguiremos tres bloques principales: 1) Paralelización de problemas simples como ecuaciones lineales, inversión de matrices y problemas no lineales. 2) Optimización distribuida y aprendizaje estadístico abordando técnicas de optimización de redes como consenso, difusión y chismes. 3) Paralelización de la optimización con datos masivos utilizando métodos basados ​​en el descenso de coordenadas.

Statistical modelling

3 ECTS

Description

This course provides the foundations of statistical analysis of data as fundamental background for the study of machine learning techniques. The course reviews the probability theory as well as basic concepts and tools for descriptive statistic and statistical inference.

Time series analysis

4.5 ECTS

Description

This course is an introduction to the theory and practice of time series analysis, providing statistical tools to analyze random data that are ordered in time.

Predictive and descriptive learning

6 ECTS

Description

This course covers the principles and methodology for the design, evaluation and selection of a large variety of Machine Learning methods for supervised and unsupervised learning. Special emphasis is placed on algorithms suitable for parallel implementation to manage very large-scale data.

Data science foundations and applications

2 ECTS

Description

This course aims to help students to gain a global perspective of all the courses in this Track. Students will learn to critically assess the value of different scientific and technological approaches to derive knowledge from data in real-world applications. Case studies and debates are addressed over a set of conferences bringing together leading experts in different sectors: Cognitive Radio and Networks, Future Internet Services, Social Networks and Multimedia Analytics, Internet-of-Things, Machine-to-Machine, Smart Cities, Smart Grids, Biomedical Applications, Biometrics and Forensics, Financial Services, Robotic systems... 8 Signal Processing for Big Data This course addresses new challenges of signal processing when applied on large-scale data, including processing algorithms for large-scale sparse data, as the Sparse Fourier transform, discrete signal processing on graphs (DSPG), and the use of Tensors for analyzing Big Data.

Machine learning lab

4.5 ECTS

Description

In this lab students will consolidate their knowledge of theories and fundamental background received along the first semester of the Master. Particular attention is given to experimental work on Big Data platforms and tools (Hadoop, Spark, H2O, R, Python, Scala,...) applied over practical large-scale problems. Several real use cases are considered, mainly working with financial time series, sensor signals, and speech and audio signals.

Large scale media analytics

4 ECTS

Description

This subject aims at presenting the most relevant techniques and methodologies to analyze multimedia collections at large scale. Several use cases are studied: large-scale content search and retrieval, automatic classification and content understanding, and hybrid (content-based and collaborative) multimedia recommendation systems.

Signal processing for big data

4 ECTS

Description

This course presents a selection of the most recent and relevant techniques for massively processing, transmitting and storing images and video. It addresses new concepts as new signal sampling frameworks, Compressive Sensing, Random projections, and introduces innovative practical use cases such as the intuitive visualization of high dimensional data and applications in the computer vision field.

Big data for image and video signals

4 ECTS

Description

This course presents a selection of the most recent and relevant techniques for massively processing, transmitting and storing images and video. It addresses new concepts as new signal sampling frameworks, Compressive Sensing, Random projections, and introduces innovative practical use cases such as the intuitive visualization of high dimensional data and applications in the computer vision field.

Bio-inspired learning

3 ECTS

Description

The goal of this course is to develop biologically-inspired approaches to Machine Learning, including Artificial Neural Networks, Deep Neural Networks and Swarm Systems. The course also introduces concepts and applications of System of System Engineering and Simulation of Intelligent Systems.

Reinforcement learning

3 ECTS

Description

This course presents the motivation, fundamentals and different existing methods for solving Reinforcement Learning problems. Several practical use cases related to distributed communication and sensor networks are considered along the course.

Application projects

4 ECTS

Description

In this course students can reinforce their learning experience working on practical projects that they can select from a broad range of areas: cognitive radio, sensor networks, social sensors, social graphs, sentiment analysis, smartphone sensors and social networks, smart cities, multimedia analytics (image, video, speech, music and audio), biometrics and forensic analytics, biomedical engineering, financial trading, environmental monitoring, robotics systems... This course will also propose Kaggle-style competitions (www.kaggle.com) where students will form teams and work together. This will allow students to apply the concepts learned in the different courses and develop the computational skills to analyze data in a collaborative setting.

Master's Thesis (MT)

12 ECTS

Description

The Final Master Thesis offers a great opportunity to closely collaborate with your favorite professor on advanced topics to be agreed with him/her. Located in the second semester with 12 ECTS, at that time you will have the global perspective about which are the more challenging topics or more demanded by local or international companies you desire to deepen. After an initial discussion with your academic advisor about your preferences, he/she will put in contact with the most suitable researcher among our professors who will lead your final stage before obtaining the final degree. You will have the chance to access to the professional laboratories in our department and get involved in our research or our technological transfer projects. Finally you will have to elaborate an extended report describing your achievements and make an oral presentation defending your results in front of other professors experts in the field. In the end, you will have gained full professional maturity to continue your career in academia or high technological companies with unique capabilities.

Practical information

Pre-registration

The University establishes the enrollment dates that are published in:

Estudios Oficiales de Máster

Official Master’s Degree Studies

Calendar and schedule

Exams

Misc

Cooperating partners

Testimonials

The most relevant feature I'd like to stress about MSTC is the balanced combination of theory and practice. Throughout my undergraduate studies I felt problems are faced from the theoretical perspective, whereas in MSTC they are faced from both the theory and practice viewpoints, with a clear application to the real engineering world.
Raquel Dueñas Suárez - Data scientist en SGAE
MTSC gave me a higher specialisation in the signal processing and machine learning areas, besides critical thinking to tackle different problems in AI and business.
Carlos Rodríguez Abellán - Senior Data Scientist en Telefónica
From the SSR department I can highlight the professional attitude, the wide knowledge from the faculties and the tight relationship between students and faculties.
Xiaolian Sun - Especialista en medidas de antenas en LEHA (Laboratoratorio de Ensayos y Homologación de Antenas de la UPM)
After 13 years working in the satellite communications sector, I was looking for expanding my knowledge. Finding this M.Sc. degree was exactly what I needed. I knew that UPM was the university which was going to provide me a high quality learning programme.
Ekhi Uranga - Centro Europeo de Astronomía Espacial (ESAC) de la Agencia Europea del Espacio (ESA)
Next to the fact that I enjoyed the content of the courses of UPM's MSTC, I also found the quality of education very good and got motivated by the enthusiasm of the professors. Also, I have been truly touched by their dedication and willingness to assist students. All in all, my time at UPM exceeded all my expectations.
Tessa Mennink - Delft University of Technology

Contact

MSTC Coordinator: Francisco Morán Burgos

Email: master.tsc@upm.es

Telephone: +34 9106 72303