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 theorem. 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.