Telecommunication Engineering

Activity type

Ingegneria delle telecomunicazioni

SSD

ING-INF/03

Year

N/D

Semester

N/D

CFU

6

Hours distribution

36 classroom hours, 24 training hours

Lecturers

PAOLO DI LORENZO

Activity type

Ingegneria delle telecomunicazioni

SSD

ING-INF/03

Year

N/D

Semester

N/D

CFU

6

Hours distribution

36 classroom hours, 24 training hours

Lecturers

MAURO BIAGI

GENERAL
The course aims to provide a solid and integrated foundation in the fundamental principles of information theory, coding, and statistical signal processing, with attention to both theoretical aspects and practical applications in modern digital communication systems. By the end of the course, students will be able to understand key concepts such as entropy and channel capacity, evaluate the efficiency of source and error-correcting codes, and interpret the implications of Shannon’s theorems. The course also covers the fundamentals of statistical signal processing and the theory of estimation and detection, including maximum likelihood and Bayesian approaches. Furthermore, it explores advanced techniques for signal transmission and reception, such as channel equalization, multicarrier systems like OFDM, synchronization, channel estimation, diversity techniques, and multi-antenna systems for communication under fading conditions. The course fosters the ability to model communication problems mathematically and encourages a quantitative and critical approach, supported by hands-on exercises using MATLAB and/or Python.

SPECIFIC
• Knowledge and understanding: Students acquire a solid understanding of the principles of information theory, coding, and statistical signal processing, with applications to physical-layer digital communication systems.
• Applying knowledge and understanding: Students are able to apply models and techniques from information and signal processing theory to analyze, design, and simulate efficient and reliable communication systems, adapted to the characteristics of the source and the channel.
• Making judgements: Students develop the ability to critically assess the performance of various coding, estimation, and detection methods, and to choose the most appropriate strategies based on the application context and operational conditions.
• Communication skills: Students gain the technical language required to clearly describe models, algorithms, performance metrics, and design choices in the field of digital communications.
• Learning skills: Students are able to independently explore advanced topics in communication and signal processing, building skills that are valuable for further academic or professional development.

At the end of the course, the student will have acquired a solid understanding of the fundamental principles of information theory, coding, and statistical signal processing, developing the ability to critically and quantitatively address issues related to the transmission and reception of signals in modern digital communication systems. They will be able to interpret key concepts such as entropy, channel capacity, and Shannon’s theorem, and apply this knowledge to evaluate the efficiency of codes used for data compression and error correction. The student will be capable of analyzing and modeling random signals using advanced statistical methods, including maximum likelihood and Bayesian estimation techniques, and will understand how to design optimal detection systems. They will also be equipped to tackle the challenges posed by real-world transmission channels, gaining skills in equalization, synchronization, multi-carrier systems such as OFDM, channel estimation, and in employing diversity techniques and multi-antenna systems to mitigate fading effects. Thanks to hands-on exercises using MATLAB and/or Python, the student will also be able to simulate and analyze complex scenarios, translating theoretical models into practical implementations. In summary, they will develop the ability to approach engineering problems with a rigorous mindset, modeling them mathematically and proposing solutions based on quantitative and experimental criteria.

Basic knowledge of mathematical analysis, linear algebra, probability theory, random variables, signal theory, and digital communications.

Module: INFORMATION THEORY AND CODING
Part 1 – Information Theory and Coding (60 hours)

Fundamentals of Information, Source, and Channel (30 hours)
Measurement of information (entropy), coding of discrete sources (independent/dependent symbols, Markov sources).
Data, audio, and image compression (Ziv-Lempel, MP3, JPEG).
Models of discrete and binary channels, concepts of channel capacity, mutual information, Shannon’s theorems, Fano’s inequality, information-theoretic security.


Coding Techniques for Error Detection and Correction (15 hours)
Codes for error detection (parity check, checksum, CRC, ARQ).
Codes for error correction (FEC, interleaved codes).
Block and linear codes: construction (generator and parity-check matrices), cyclic and dual codes.
Well-known codes: Hamming, Golay, BCH, Reed-Solomon, maximum-length codes.


Advanced Coding and Decoding Techniques (15 hours)
Convolutional codes: representation, error correction capability, Viterbi decoding (hard/soft decision), interleaving and concatenation.
Turbo codes: RSC codes, puncturing, parallel concatenation, iterative decoding.
Recent techniques: Trellis Coded Modulation (TCM), Low-Density Parity-Check (LDPC) codes, Space-Time Block Codes (STBC), CDMA.


Part 2 – Signal Processing for Communications (60 hours)

Estimation Theory (20 hours):
Properties of estimators: unbiasedness, efficiency, consistency.
Minimum Variance Unbiased Estimation. Cramer-Rao lower bound.
Linear models. Sufficient statistics. Maximum Likelihood estimation. Least squares.
Bayesian Estimation, Linear MMSE estimation.
Application to carrier phase and symbol timing estimation in communications.

Detection Theory (10 hours):
Neyman-Pearson Theorem. Minimum Probability of Error. Bayes Risk. Multiple Hypothesis Testing.
Detection of deterministic signals: Matched filters.
Detection of random signals: The Estimator-Correlator.

Equalization and multi-carrier systems (18 hours):
Channels as LTI and LTV systems. Examples of channel models.
Optimum receivers for channels with ISI and AWGN, Maximum likelihood sequence estimation.
Block transmission systems, symbol detection, guard intervals, linear equalization (zero forcing, MMSE, adaptive LMS).
Orthogonal frequency division multiplexing (OFDM): Modulation and demodulation, cyclic prefix, digital implementation using Discrete Fourier Transform. Synchronization issues. Channel Estimation.

Diversity and multi-antenna communications (12 hours):
Wireless channels: Shadowing, multipath fading. SISO and MIMO channels.
The effect of Fading. Outage Probability. Average Probability of Error. Receiver and Transmitter diversity.
Multi-antenna communications, MIMO symbol detection, Multiplexing gain, MIMO beamforming, Multiplexing-Diversity trade-off.



Module: SIGNAL PROCESSING FOR COMMUNICATIONS
Part 1 – Information Theory and Coding (60 hours)

Fundamentals of Information, Source, and Channel (30 hours)
Measurement of information (entropy), coding of discrete sources (independent/dependent symbols, Markov sources).
Data, audio, and image compression (Ziv-Lempel, MP3, JPEG).
Models of discrete and binary channels, concepts of channel capacity, mutual information, Shannon’s theorems, Fano’s inequality, information-theoretic security.


Coding Techniques for Error Detection and Correction (15 hours)
Codes for error detection (parity check, checksum, CRC, ARQ).
Codes for error correction (FEC, interleaved codes).
Block and linear codes: construction (generator and parity-check matrices), cyclic and dual codes.
Well-known codes: Hamming, Golay, BCH, Reed-Solomon, maximum-length codes.


Advanced Coding and Decoding Techniques (15 hours)
Convolutional codes: representation, error correction capability, Viterbi decoding (hard/soft decision), interleaving and concatenation.
Turbo codes: RSC codes, puncturing, parallel concatenation, iterative decoding.
Recent techniques: Trellis Coded Modulation (TCM), Low-Density Parity-Check (LDPC) codes, Space-Time Block Codes (STBC), CDMA.


Part 2 – Signal Processing for Communications (60 hours)

Estimation Theory (20 hours):
Properties of estimators: unbiasedness, efficiency, consistency.
Minimum Variance Unbiased Estimation. Cramer-Rao lower bound.
Linear models. Sufficient statistics. Maximum Likelihood estimation. Least squares.
Bayesian Estimation, Linear MMSE estimation.
Application to carrier phase and symbol timing estimation in communications.

Detection Theory (10 hours):
Neyman-Pearson Theorem. Minimum Probability of Error. Bayes Risk. Multiple Hypothesis Testing.
Detection of deterministic signals: Matched filters.
Detection of random signals: The Estimator-Correlator.

Equalization and multi-carrier systems (18 hours):
Channels as LTI and LTV systems. Examples of channel models.
Optimum receivers for channels with ISI and AWGN, Maximum likelihood sequence estimation.
Block transmission systems, symbol detection, guard intervals, linear equalization (zero forcing, MMSE, adaptive LMS).
Orthogonal frequency division multiplexing (OFDM): Modulation and demodulation, cyclic prefix, digital implementation using Discrete Fourier Transform. Synchronization issues. Channel Estimation.

Diversity and multi-antenna communications (12 hours):
Wireless channels: Shadowing, multipath fading. SISO and MIMO channels.
The effect of Fading. Outage Probability. Average Probability of Error. Receiver and Transmitter diversity.
Multi-antenna communications, MIMO symbol detection, Multiplexing gain, MIMO beamforming, Multiplexing-Diversity trade-off.

Module: INFORMATION THEORY AND CODING
Kay "fundamentals of statistical signal processing"


Module: SIGNAL PROCESSING FOR COMMUNICATIONS
Cover and Thomas, Elements of Information Theory
Notes/Slides by Teacher

Module: INFORMATION THEORY AND CODING
Kay "fundamentals of statistical signal processing"


Module: SIGNAL PROCESSING FOR COMMUNICATIONS
[1] Cover, Thomas "Elements of Information theory"

Lectures in presence and exercises

Attendance is not mandatory, only suggested

The written part of the exam is an exercise on the first part and question(s) on the second one. (10/30)
The oral exam is focused on both parts. (20/30)

Minimum Variance Unbiased Estimation
Neyman-Pearson Theorem
Linear equalization
OFDM
Diversity
MIMO communications
mutual information and channel capacity
block codes
convolutional codes

Module: INFORMATION THEORY AND CODING

• Estimation theory


• Detection theory


• Equalization for multi-carrier systems


• Multi-Antenna Systems

• source coding


• channel coding


• fundamental limits of inofrmation theory