Checking date: 26/04/2019


Course: 2019/2020

Advanced Communications
(15028)
Study: Master in Advanced Communications Technologies (278)
EPI


Coordinating teacher: MARTÍNEZ OLMOS, PABLO

Department assigned to the subject: Department of Signal and Communications Theory

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Students are expected to have completed
In this course the students should use basic notions of: - Communication Theory, - Digital Communications - Linear Algebra and - Statistics.
Competences and skills that will be acquired and learning results.
Generic skills to be acquired are listed below. When matching some of the program outcomes of the Bachelor Degree, this is indicated within brackets. 1. Ability to analyze and synthesize. 2. Ability to apply knowledge of mathematics, statistics, science, telecommunication technologies and engineering. 3. Ability to identify, formulate and solve engineering problems. 4. Ability to effectively communicate information in speech, presentation and in writing. 5. Ability for team work. Specific technical skills: 1. Ability to understand the limitations of classic channel coding techniques. 2. Ability to implement iterative algorithms for approximate inference. Application to LDPC channel decoding problem. 3. Ability to analyze the characteristics of wireless channels and understand the consequences in the design of digital communications systems over such channels. 4. Ability to synthesize when reading and analyzing scientific papers on wireless communications. 5. Ability to apply concepts of signal processing and linear algebra to analyze and design MIMO systems.
Description of contents: programme
This course consists in advanced digital communications with a focus on the encoding and decoding techniques that allow us to approach the limits set by the information theory, and the analysis of these limits in the case of wireless communications, relating the physical characteristics of the channels and setting the receptors with the achievable transmission rate. The course will address at least the following content: Finite-length information theory Inference in Graphical Models -Sum Product Algorithm. Bethe approximations. -Variational Inference -Expectation Propagation -Monte Carlo Methods Advanced coding techniques: LDPC codes. -Iterative decoding algorithms: Graphs and trees. -Message passing algorithms: Belief Propagation. -LDPC decoding. Synchronization Techniques and Channel Estimation
Learning activities and methodology
Three types of activities are used: lectures, seminars and practical projects. The assessment of these activities is complemented with two short written exams. Details on the evaluation and grading are given in next section. LECTURES Lectures are used to expose the major theoretical concepts, combined with exercises consisting of the design of illustrative algorithms. The instructor uses the blackboard (for mathematics) combined with specific audio-visual presentations for specific topics. SEMINARS The student will participate by solving different problems. In this way, the student can assimilate the concepts addressed in the lectures and self-assess his/her progress. The list of problems to be studied in each seminar will be available before hand. We consider two types of problems: There will be two types of problems to be studied in these sessions. · "Closed problems": They are the classical problems and exercises with a clearly defined solution that the solutions should work out individually. · "Open problems": They admit several solutions. They require that the students identify, formulate and propose solutions for several interacting subproblems. The students will tackle these problems working in teams. The teacher will collect some of these open problems for evaluation. LABORATORY PROJECTS Three projects are conducted during the course, related to the contents of LDPC coding and simulation of a simple MIMO channel. Both projects are done in teams of two or three students. Include the design, simulation and measurement (using numerical software) of some of the algorithms in the lectures and problems. Each team delivers simulation code and a brief written report and make a brief presentation of the projects to the teacher and other students.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • Andrea Goldsmith. Wireless Communications. Cambridge University Press. 2005
  • David Tse, Pramod Viswanath. Fundamentals of Wireless Communication. Cambridge University Press. 2005
  • Todd K. Moon. Error Correction Coding: Mathematical Methods and Algorithms. Wiley-Interscience. 2005
  • Tom Richardson, Ruediger Urbanke. Modern Coding Theory. Cambridge University Press. 2008

The course syllabus and the academic weekly planning may change due academic events or other reasons.