Array Processing
Code: 42844 ECTS Credits: 6| Degree | Type | Year | Semester |
|---|---|---|---|
| 4313797 Telecommunications Engineering | OT | 2 | 1 |
Contact
- Name:
- Francesc Xavier Mestre Pons
- Email:
- Desconegut
Use of Languages
- Principal working language:
- english (eng)
Prerequisites
For students who have been admitted indirectly to the master (e.g. those who must attend complementary courses), they should have already passed the course on "Tractament digital del senyal" (TDS) offered within the B.Sc. degree on Telecommunication Systems Engineering (i.e. "Grau d'Enginyeria en Sistemes de Telecomunicació"). Basic knowledge on Matlab programming is also required.
Objectives and Contextualisation
The objective of this course is to introduce traditional methods of array signal processing for multi-antenna transceivers, including spatial filtering (beamforming), direction of arrival estimation and multiple-input multiple-output (MIMO) communication systems.
The use of transmitters and/or receiver with multiple antennas is nowadays widespread in modern wireless communications, localization and radar systems. The trend of increasing the number of antenna in any device will likely continue in the next years. After this course, the student will have the understanding of the fundamental concepts of array signal processing and the capability to apply them to the design of future telecommunications and positioning systems.
Competences
- Capacity for applying theory of information methods, adaptative modulation and channel coding as well as advanced techniques for digital signal processing in telecommunications and audiovisual systems.
- Capacity for critical reasoning and thought as means for originality in the generation, development and/or application of ideas in a research or professional context.
- Capacity for designing radionavegation, positioning systems and radar systems.
- Capacity for developing radio communications systems: design of antennas, equipment and subsystems, channel modelling, calculation of links and planning.
- Capacity to integrate new technologies and systems developed within telecommunications engineering in general and in broader, multidisciplinary contexts such as bioengineering, photovoltaic conversion, nanotechnology, telemedicine
- Possess and understand knowledge that provides a basis or opportunity for originality in the development and/or application of ideas, often in a research context
- Student should possess the learning skills that enable them to continue studying in a way that is largely student led or independent
- Students should know how to apply the knowledge they have acquired and their capacity for problem solving in new or little known fields within wider (or multidisciplinary) contexts related to the area of study
- Students should know how to communicate their conclusions, knowledge and final reasoning that they hold in front of specialist and non-specialist audiences clearly and unambiguously
Learning Outcomes
- Analyse the implications at system level of the use of antenna arrays in different applications.
- Apply the principal techniques of smart beam conformation, estimation of the direction multichannel arrival and MIMO communications.
- Capacity for critical reasoning and thought as means for originality in the generation, development and/or application of ideas in a research or professional context.
- Classify the different algorithms in array signal processing and use them to describe the main characteristics of multichannel techniques.
- Describe the different ways of modeling the signals received in an array.
- Design in array processing algorithms that solve specific problems in the field of communications or other related telecommunications engineering fields.
- Evaluate the advantages of using multiple antennas for the reception and/or transmission of radionavegation and radar systems.
- Possess and understand knowledge that provides a basis or opportunity for originality in the development and/or application of ideas, often in a research context
- Put into practice different techniques using real or synthetic signal samples.
- Student should possess the learning skills that enable them to continue studying in a way that is largely student led or independent
- Students should know how to apply the knowledge they have acquired and their capacity for problem solving in new or little known fields within wider (or multidisciplinary) contexts related to the area of study
- Students should know how to communicate their conclusions, knowledge and final reasoning that they hold in front of specialist and non-specialist audiences clearly and unambiguously
Content
1. Introduction to array processing
1.1. Baseband signal model and analytic signal.
1.2. Far field and near field models. Narrowband approximation.
1.3. Direction of arrival. Spatial covariance matrix. Source coherence.
2. Spatial filtering
2.1. Space-time filtering and beamforming.
2.2. Design of time reference beamformers. Communication applications.
2.3. Design of spatial reference beamformers. Radar/sonar applications.
2.4. Other training methods for spatial filtering.
3. Direction of arrival (DoA) estimation
3.1. Main principles in DoA estimation.
3.2. Phased arrays and spatial periodogram.
3.3. Subspace-based techniques. MUSIC.
3.4. Spatial prediction: ESPRIT.
3.5. High resolution methods: maximum likelihood and approximations.
4. Multiple-input Multiple-output processing: spatial diversity and multiplexing
4.1. Spatial diversity at the transmitter and at the receiver.
4.2. Space-time coding.
4.3. Introduction to Information Theory for multi-antenna system. MIMO capacity.
4.4. Optimum spatial processing. Waterfilling.
5. Examples of array signal processing in 5G wireless communications systems, MIMO radar, and positioning systems.
Methodology
Lectures: development of the theoretical concepts of the course.
Laboratory: development of Matlab-based exercises covering the theoretical contents of the course.
Student self-learning activities: Study of the material presented during the lectures. Preparation of lab exercises, other homework and/or exams.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Lectures | 30 | 1.2 | 1, 2, 7, 4, 5, 6, 9, 11, 10, 8 |
| Study, preparations of problems and laboratori sessions | 86 | 3.44 | 1, 2, 7, 3, 4, 5, 6, 9, 11, 12, 10, 8 |
| Type: Supervised | |||
| Laboratory and problem sessions | 15 | 0.6 | 1, 2, 7, 3, 4, 5, 6, 9, 11, 12, 10, 8 |
Assessment
The final evaluation will take into account the lab reports and exercises (30%), and a final report of a research project on one of the topics of the course, to be agreed with the lecturer (70%).
FinalGrade = max (Project Report, 0.7*Project Report + 0.3*Exercises).
The course will be declared to be passed when FinalGrade >= 5.
If FinalGrade < 5, students will have a second chance to pass the course by doing a final exam. The grade of the course will be the maximum between the grade of the exam and the FinalGrade previously obtained.
Students missing both the Project Report and the Final Exam will be declared to be "Not Graded" in the final course evaluation.
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Personal project and report on some topic of the course | 70% | 15 | 0.6 | 1, 2, 7, 3, 4, 5, 6, 9, 11, 12, 10, 8 |
| Reports from laboratory sessions and problem solutions | 30% | 4 | 0.16 | 1, 2, 7, 3, 4, 5, 6, 9, 11, 12, 10, 8 |
Bibliography
H. Van Trees, Optimum Array Processing, part IV of Detection, Estimation and Modulation Theory, New York, Wiley 2002.
Don H. Johnson, Dan E. Dudgeon, Array Signal Processing, Concepts and Techniques, Prentice Hall, 1993.
E. Larsson, P. Stoica, Space-time block coding for wireless communications, Cambridge University Press, UK, 2003.
S. Haykin, Array signal processing, Prentice Hall, Englewood Cliffs, NJ, 1985.
P. Stoica and R. Moses, Spectral Analysis of Signals, Prentice Hall, NJ, 2005.
Steven M. Kay, Fundamentals of Statistical Signal Processing, Prentice Hall, 1993.