THREE-DIMENSIONAL MODELING

Course objectives

GENERAL The module aims to provide students with a solid understanding of the fundamental principles of information theory and coding. By the end of the course, students will be able to understand the concept of entropy as a measure of information, analyze the efficiency of compression codes (such as Huffman and Shannon-Fano), and evaluate the performance of error detection and correction codes, such as linear codes. They will also be able to interpret the meaning and implications of Shannon’s theorem and apply these concepts to the study of communication channel capacity. The course also aims to develop the ability to model communication problems in mathematical terms, fostering a quantitative and logical-deductive approach to the design and analysis of systems for the efficient and reliable transmission of information. Particular attention is devoted to both theoretical aspects and practical applications in modern digital communication systems. SPECIFIC • Knowledge and understanding: The student acquires a solid understanding of the fundamentals of information theory and coding for compression and error correction. • Applying knowledge and understanding: The student is able to apply theoretical principles to analyze and design efficient and reliable digital communication systems. • Making judgements: The student develops the ability to independently evaluate the most appropriate solutions based on the characteristics of the source and the communication channel. Communication skills: The student acquires the technical language needed to clearly describe models, codes, and results in the context of information transmission. • Learning skills: The student is able to independently explore advanced concepts in information theory and coding for academic or professional development.

Channel 1
MAURO BIAGI Lecturers' profile

Program - Frequency - Exams

Course program
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.
Prerequisites
Signal theory and digital modulations
Books
Cover and Thomas, Elements of Information Theory Notes/Slides by Teacher
Frequency
Attendance is strongly recommended, but not mandatory.
Exam mode
The exam consists of a single assessment covering both modules, which includes a written and an oral part.
Bibliography
[1] Cover, Thomas "Elements of Information theory"
Lesson mode
The course is in presence
  • Academic year2025/2026
  • CourseTelecommunication Engineering
  • CurriculumTelecommunication Engineering (percorso valido anche ai fini del rilascio del doppio titolo italo-francese o italo-statunitense )
  • Year1st year
  • Semester1st semester
  • SSDING-INF/03
  • CFU6