Important notice
The course guide is provisional.
The PDF version of the course guide may take a few days to become available in the DDD.
Multichannel Signal Processing
Code: 45641Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Telecommunication Engineering | OB | 1 |
Contact lecturer
- Name :
- Francesc Xavier Mestre Pons
- Email :
- francescxavier.mestre@uab.cat
Group languages
You can consult this information at the end of the document.
Prerequisites
It is highly recommended that students have successfully completed the course Detection and Estimation, offered as part of this Master's Degree.
Objectives
The objective of this course is to introduce the fundamental principles and classical methods of array signal processing for multi-antenna transceivers. The course covers key techniques such as spatial filtering (beamforming), direction-of-arrival (DoA) estimation, and multiple-input multiple-output (MIMO) communication systems. Emphasis is placed on the underlying signal processing concepts, mathematical foundations and practical algorithms that enable efficient wireless communication, interference suppression and spatial sensing in modern multi-antenna systems.
Learning outcomes
- CA06 (Implement digital mobile communications systems with multi-antenna equipment, prioritizing solutions that improve energy efficiency and reduce the environmental impact of infrastructures.) Implement digital mobile communications systems with multi-antenna equipment, prioritizing solutions that improve energy efficiency and reduce the environmental impact of infrastructures.
- CA07 (Examine the differences between conventional signal processing and multi-channel signal processing, and integrate multi-channel technologies into broader contexts, such as multi-sensor systems and data fusion.) Examine the differences between conventional signal processing and multi-channel signal processing, and integrate multi-channel technologies into broader contexts, such as multi-sensor systems and data fusion.
- CA08 (Apply advanced multi-channel processing techniques in communications systems, with the aim of improving efficiency and accuracy in the handling of multi-channel signals.) Apply advanced multi-channel processing techniques in communications systems, with the aim of improving efficiency and accuracy in the handling of multi-channel signals.
- KA06 (Identify signal models, estimation and shaping techniques used in digital systems with multiple antennas.) Identify signal models, estimation and shaping techniques used in digital systems with multiple antennas.
- KA07 (Describe the optimal and suboptimal communication schemes applicable in digital transmitters and receivers with multiple antennas, as well as the different gains associated with each of them) Describe the optimal and suboptimal communication schemes applicable in digital transmitters and receivers with multiple antennas, as well as the different gains associated with each of them
- KA08 (Select the most appropriate technique for direction of arrival estimation, pre-coding and beamforming based on the requirements of the final application.) Select the most appropriate technique for direction of arrival estimation, pre-coding and beamforming based on the requirements of the final application.
- SA10 (Apply methods from information theory, channel coding, and advanced digital signal processing techniques in multi-antenna digital systems.) Apply methods from information theory, channel coding, and advanced digital signal processing techniques in multi-antenna digital systems.
- SA11 (Design digital communications algorithms in systems that use multiple antennas in the transmitter and/or receiver.) Design digital communications algorithms in systems that use multiple antennas in the transmitter and/or receiver.
- SA12 (Apply multi-channel processing techniques to radio navigation, positioning and radar systems.) Apply multi-channel processing techniques to radio navigation, positioning and radar systems.
- SA13 (Apply the models that represent the propagation, generation and reception of signals in multiple channels.) Apply the models that represent the propagation, generation and reception of signals in multiple channels.
- SA14 (Prepare reports on the application of multichannel processing to different cases of use and its generalisation to other domains, such as frequency.) Prepare reports on the application of multichannel processing to different cases of use and its generalisation to other domains, such as frequency.
Contents
1. Introduction to Array Processing
1.1. Baseband signal model and analytic signal.
1.2. Far-field and near-field propagation models. Narrowband approximation.
1.3. Direction of arrival (DoA). Spatial covariance matrix. Source coherence.
2. Spatial Filtering
2.1. Space-time filtering and beamforming.
2.2. Design of temporal-reference beamformers. Communication applications.
2.3. Design of spatial-reference beamformers. Radar and sonar applications.
2.4. Adaptive beamforming and other training methods for spatial filtering.
3. Direction-of-Arrival (DoA) Estimation
3.1. Fundamental principles of DoA estimation.
3.2. Phased arrays and the spatial periodogram.
3.3. Subspace-based methods: MUSIC.
3.4. Rotational invariance methods: ESPRIT.
3.5. High-resolution methods: maximum likelihood estimation and approximations.
4. Multiple-Input Multiple-Output (MIMO) Signal Processing: Spatial Diversity and Multiplexing
4.1. Transmit and receive diversity.
4.2. Space-time coding.
4.3. Introduction to information theory for multi-antenna systems. MIMO channel capacity.
4.4. Optimum MIMO processing. Water-filling.
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Independent student work: Study of the material presented during the lectures, and preparation of laboratory exercises, other assignments, and examinations. | 105 | 4.2 | CA06, CA07, CA08, KA06, KA07, KA08, SA10, SA11, SA12, SA13, SA14 |
| Laboratory sessions: Development of MATLAB-based exercises covering and reinforcing the theoretical concepts presented in the course. | 14 | 0.56 | CA06, CA08, KA07, KA08, SA11, SA12, SA13, SA14 |
| Lectures: Presentation and development of the theoretical concepts covered in the course. Assessment is based on written examinations. | 26 | 1.04 | CA06, CA07, CA08, KA06, KA07, KA08, SA10, SA11, SA12, SA13, SA14 |
Lectures: Presentation and development of the theoretical concepts covered in the course. Written assessment through exams.
Laboratory sessions: Development of MATLAB-based exercises illustrating and reinforcing the theoretical concepts presented in the lectures.
Independent student work: Study of the lecture material and preparation of laboratory assignments and examinations.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| First Mid-term exam | 33% | 2 | 0.08 | CA06, CA07, CA08, KA06, KA07, KA08, SA10, SA11, SA12, SA13, SA14 |
| Second mid-term exam | 33% | 2 | 0.08 | CA06, CA07, CA08, KA06, KA07, KA08, SA10, SA11, SA12, SA13, SA14 |
| Lab sessions | 34% | 1 | 0.04 | CA06, CA07, CA08, KA06, KA07, KA08, SA10, SA11, SA12, SA13, SA14 |
The final grade for the second part of the course will be based on two written examinations (E₁ and E₂) and a set of lab exercises (L).
The final grade (G) will be computed as the maximum of the following two expressions: (i) the average of the two examination grades, (0.5E₁ + 0.5E₂), and (ii) the weighted combination (0.33E₁ + 0.33E₂ + 0.34L).
The course will be considered passed if the final grade (G) is greater than or equal to 5 out of 10. If this grade is below 5/10, students may take an optional retake examination (E), which will replace both E₁ and E₂. In this case, the final grade will be calculated as the maximum of the grade obtained in the retake examination (E) and the weighted combination (0.66E + 0.34L).
Bibliography
• S. Kay, Fundamentals of statistical signal processing. Estimation theory, vol. I, Prentice-Hall, 1993.
• S. Kay, Fundamentals of statistical signal processing. Detection theory, vol. II, Prentice-Hall, 1998.
• Don H. Johnson, Dan E. Dudgeon, Array Signal Processing, Concepts and Techniques, Prentice Hall, 1993.
• S. Haykin, Array signal processing, Prentice Hall, Englewood Cliffs, NJ, 1985.
• E. Larsson, P. Stoica, Space-time block coding for wireless communications, Cambridge University Press, UK, 2003.
• A. Lee Swindlehurst et al., Applications of Array Signal Processing. In: R. Chellappa and S.Theodoridis, editors, Academic Press Library in Signal Processing. Vol 3, Array and Statistical Signal Processing. Online: http://spcomnav.uab.es/docs/chapters/ApplicationsofArraySignalProcessing.pdf
• H. Van Trees, Optimum Array Processing, part IV of Detection, Estimation and Modulation Theory, New York, Wiley 2002.
Software
The laboratory sessions will make extensive use of MATLAB to implement and illustrate the theoretical concepts presented in the lectures.
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (TEmRD) Teoria (màster RD) | 1 | English | second semester | afternoon |
| (PLABmRD) Pràctiques de laboratori (màster RD) | 1 | English | second semester | afternoon |