THREE-DIMENSIONAL MODELING

Course objectives

This module provides a solid foundation in statistical signal processing and advanced physical layer communication techniques, with emphasis on theory and practical application. Students explore core concepts in estimation and detection theory, including properties of estimators, maximum likelihood and Bayesian approaches, and decision-making under uncertainty. The course then focuses on advanced communication techniques, examining how signals are transmitted and received over various channel models. Topics include channel equalization strategies, multi-carrier systems like OFDM, synchronization and channel estimation. Additionally, the course covers diversity techniques and multi-antenna systems, exploring how methods like MIMO and beamforming enhance communication reliability and performance in fading environments. Practical experience is emphasized through programming assignments using MATLAB and/or Python, enabling students to simulate and analyze real-world systems. By the end of the course, students will be equipped to understand, apply, and critically assess signal processing methods in communication contexts, and will be prepared for further study or research in the field. SPECIFIC • Knowledge and understanding: The student acquires a solid background in statistical estimation and detection theory, and understands their applications in physical-layer communication problems. • Applying knowledge and understanding: The student can apply signal processing and digital communication techniques to simulate and evaluate advanced communication systems. • Making judgements: The student critically assesses the performance and limitations of different estimation/detection techniques and communication strategies under various channel conditions. Communication skills: The student clearly presents technical concepts and results related to signal processing for communications, including performance metrics and system design choices. • Learning skills: The student builds a solid foundation for further studies or research in communication systems and statistical signal processing.

Channel 1
PAOLO DI LORENZO 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.
Books
Kay "fundamentals of statistical signal processing"
Bibliography
Kay "fundamentals of statistical signal processing"
  • 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