Servizi per la didattica

PORTALE DELLA DIDATTICA

01QWJBG

A.A. 2019/20

Course Language

English

Course degree

Master of science-level of the Bologna process in Communications And Computer Networks Engineering - Torino

Course structure

Teaching | Hours |
---|---|

Lezioni | 80 |

Teachers

Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
---|---|---|---|---|---|---|---|

Taricco Giorgio | Professore Ordinario | ING-INF/03 | 40 | 0 | 0 | 0 | 4 |

Teaching assistant

Context

SSD | CFU | Activities | Area context |
---|---|---|---|

ING-INF/03 | 8 | B - Caratterizzanti | Ingegneria delle telecomunicazioni |

2019/20

The goal of the course is providing a description of the fundamental features and technologies for digital communication systems.
During the course, the basic characteristics of the additive Gaussian channel model will be described along with an information theoretical analysis and interpretation of the achievable transmission rate. Basic concepts on information and modulation theory will be illustrated in this framework. An introduction to coding will be provided, covering both block codes and convolutional codes. The key features of error correction and error detection will be studied. The advantages given by coding will be analysed, and applications to most important communication systems will be presented.

The goal of the course is providing a description of the fundamental features and technologies for digital communication systems.
During the course, the basic characteristics of the additive Gaussian channel model will be described along with an information theoretical analysis and interpretation of the achievable transmission rate. Basic concepts on information and modulation theory will be illustrated in this framework. An introduction to coding will be provided, covering both block codes and convolutional codes. The key features of error correction and error detection will be studied. The advantages given by coding will be analysed, and applications to most important communication systems will be presented.

Knowlegde of the signal space representation and of the modulations for the additive Gaussian channel
Knowledge of the optimum Bayesian receiver and its performance analysis
Knowledge of the performance of standard digital modulations
Knowledge of information theoretical metrics
Knowledge of channel capacity and Shannon’s capacity formula
Knowledge of block encoding techniques.
Knowledge of convolutional encoding techniques.
Knowledge of basic decoding algorithms for block coding.
Knowledge of Viterbi algorithm and its application to convolutional decoding.
Knowledge of key parameters dominating coding performance and providing coding gain.
Knowledge of interleaving techniques for burst channels.
Knowledge of most important application of coding to communication systems.
Ability to design an optimum receiver over the AWGN channel
Ability to evaluate the performance of digital modulations over the AWGN channel
Ability to compare different digital modulations with different spectral efficiencies
Ability to evaluate the capacity of some classes of discrete channels
Ability to interpret the modulation performance in a Shannon diagram
Ability to choose the coding parameters for a given communication system.
Ability to evaluate a basic block and convolutional coding scheme.
Ability to design a basic block coding scheme and a basic block decoding algorithm.
Ability to design a convolutional coding scheme and a Viterbi decoding algorithm.
Ability to design an interleaver to counter the effect of burst errors.
Ability to understand the key properties of codes used in practical applications.

Knowlegde of the signal space representation and of the modulations for the additive Gaussian channel
Knowledge of the optimum Bayesian receiver and its performance analysis
Knowledge of the performance of standard digital modulations
Knowledge of information theoretical metrics
Knowledge of channel capacity and Shannon’s capacity formula
Knowledge of block encoding techniques.
Knowledge of convolutional encoding techniques.
Knowledge of basic decoding algorithms for block coding.
Knowledge of Viterbi algorithm and its application to convolutional decoding.
Knowledge of key parameters dominating coding performance and providing coding gain.
Knowledge of interleaving techniques for burst channels.
Knowledge of most important application of coding to communication systems.
Ability to design an optimum receiver over the AWGN channel
Ability to evaluate the performance of digital modulations over the AWGN channel
Ability to compare different digital modulations with different spectral efficiencies
Ability to evaluate the capacity of some classes of discrete channels
Ability to interpret the modulation performance in a Shannon diagram
Ability to choose the coding parameters for a given communication system.
Ability to evaluate a basic block and convolutional coding scheme.
Ability to design a basic block coding scheme and a basic block decoding algorithm.
Ability to design a convolutional coding scheme and a Viterbi decoding algorithm.
Ability to design an interleaver to counter the effect of burst errors.
Ability to understand the key properties of codes used in practical applications.

Calculus, linear algebra, probability, and signal theory.

Calculus, linear algebra, probability, and signal theory.

The course program is divided into two parts:
1. Digital modulations for the AWGN channel and basic concepts from Information Theory (4 credits)
(prof. Taricco)
• Analytic signal representation
• Review of basic probability concepts
• Introduction to signal spaces
• Linear modulations for the AWGN channel
• Digital receiver design
• Baseband and pass-band modulations
• Signal detection
• Error probability
• Standard digital modulations
• Power density spectrum of linear modulations
• Comparison of digital modulations: Shannon diagram
• Information theory: entropy and mutual information
• Definition of channel codes
• Discrete channels
• Discrete channel capacity
• Continuous input-continuous output channels
• Shannon capacity formula
2. Introduction to Channel Coding (4 credits)
(prof. Garello)
• Block codes
o Generating matrix and parity check matrix
o Hard and soft decoding
o Error detection
o Minimum distance, performance evaluation and coding gain
o Interleaving for burst channels
o Automatic Repeat Request
o Communication systems applications
• Convolutional codes
o Convolutional encoder, trellis representation
o Hard and soft decoding: the Viterbi algorithm
o Minimum distance, performance evaluation and coding gain
o Puncturing
o Communication systems applications

The course program is divided into two parts:
1. Digital modulations for the AWGN channel and basic concepts from Information Theory (4 credits)
(prof. Taricco)
• Analytic signal representation
• Review of basic probability concepts
• Introduction to signal spaces
• Linear modulations for the AWGN channel
• Digital receiver design
• Baseband and pass-band modulations
• Signal detection
• Error probability
• Standard digital modulations
• Power density spectrum of linear modulations
• Comparison of digital modulations: Shannon diagram
• Information theory: entropy and mutual information
• Definition of channel codes
• Discrete channels
• Discrete channel capacity
• Continuous input-continuous output channels
• Shannon capacity formula
2. Introduction to Channel Coding (4 credits)
(prof. Garello)
• Block codes
o Generating matrix and parity check matrix
o Hard and soft decoding
o Error detection
o Minimum distance, performance evaluation and coding gain
o Interleaving for burst channels
o Automatic Repeat Request
o Communication systems applications
• Convolutional codes
o Convolutional encoder, trellis representation
o Hard and soft decoding: the Viterbi algorithm
o Minimum distance, performance evaluation and coding gain
o Puncturing
o Communication systems applications

Classes alternate lectures and exercises: theoretical topics are developed during the lectures and their knowledge is tested during the exercises. Exercises are proposed to the students and subsequently solved by the lecturer.

Classes alternate lectures and exercises: theoretical topics are developed during the lectures and their knowledge is tested during the exercises. Exercises are proposed to the students and subsequently solved by the lecturer.

1. For the first part, lecture notes handouts are provided to students. The following books represent useful supplementary reading:
• S. Benedetto and E. Biglieri, Principles of Digital Transmission: With Wireless Applications. Kluwer.
• A. Goldsmith, Wireless Communications. Cambridge University Press.
• U. Madhow, Fundamentals of Digital Communication. Cambridge University Press.
• A. Molisch, Wireless Communications. Wiley.
J. Proakis and M. Salehi, Digital Communications (4th Edition).
• McGraw-Hill.
T. Rappaport, Wireless Communications: Principles and Practice (2nd Edition). Prentice-Hall.
• D. Tse and P. Viswanath, Fundamentals of Wireless Communication. Cambridge University Press.
2. For the second part,
• Stephen B. Wicker, Error Control Systems for Digital Communication and Storage, Prentice Hall
• David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press

1. For the first part, lecture notes handouts are provided to students. The following books represent useful supplementary reading:
• S. Benedetto and E. Biglieri, Principles of Digital Transmission: With Wireless Applications. Kluwer.
• A. Goldsmith, Wireless Communications. Cambridge University Press.
• U. Madhow, Fundamentals of Digital Communication. Cambridge University Press.
• A. Molisch, Wireless Communications. Wiley.
J. Proakis and M. Salehi, Digital Communications (4th Edition).
• McGraw-Hill.
T. Rappaport, Wireless Communications: Principles and Practice (2nd Edition). Prentice-Hall.
• D. Tse and P. Viswanath, Fundamentals of Wireless Communication. Cambridge University Press.
2. For the second part,
• Stephen B. Wicker, Error Control Systems for Digital Communication and Storage, Prentice Hall
• David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press

A written exam, where six to eight exercises on the two parts are proposed relevant to the course topics. The assessment will consider the correctness of the answers, the clarity and rigorousness of the development, and how exhaustive is the overall exam about the questions proposed.

The written exam is described as follow.
1. There are typically four exercises relevant to the course topics but the number may slightly change according to the complexity of the exercises proposed. The assessment will consider the correctness of the answers, the clarity and rigorousness of the development, and how exhaustive is the overall exam about the questions proposed. The questions aim at assessing the knowledge on the topics listed in the course program and the ability to apply the theoretical concepts for the solution of the exercises. There is no possibility to increment the exam grade with any other type of exam, oral or written.
2. The typical duration is 1.5 hours, exceptionally extended to 2 hours in some cases.
3. Students may not use wireless devices of any kind. Books, notes, printed material, notebooks, tablets or any device or instrument capable of storing information, are NOT ALLOWED. It is allowed the use of a hand-written A4 paper sheet (CHEAT SHEET) written on one side by the student herself or himself. No xerox copies or borrowed copies of the cheat sheet are allowed. BASIC scientific pocket calculators are allowed and recommended for the text.
4. All grades are possible from 0/30 to 30/30. The "cum laude" grade is reserved for extraordinary performances only if both teachers agree to award it. Grades are proposed to the students over a limited time period. Students are allowed to accept or reject the proposed grade if the proposed grade is at least 18/30. Accepted grades are put on record. No grade bargaining is allowed.

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Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY

Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY