The goal of this course is to introduce the basic concepts of coding, information and transmission theory, and to provide guidelines for the analysis and the design of efficient techniques for information storage and transmission over noisy channels.
Knowledge and skill in calculus and probability theory.
Classroom lectures.
Written and oral test
Information theory basic concepts: Information definition and measure. Memoryless sources and sources with memory. Shannon theorem on source coding. Examples. Basics of audio source compression (MP3). Channel models. Transmission of Information over a noisy channel. Channel capacity. Shannon theorem on channel coding. Block codes: Parity checks. Systematic and linear codes. Hamming weight and distance. Error correction and error detection capability. Finite fields (Galois) algebra: Primitive elements. Elements representation. Field operators. Extended fields. Fourier transform in finite fields. Cyclic codes. Generator polynomial. Binary and non binary codes. Encoders. BCH and Reed-Solomon codes. Algeraic decoding. Iterative decoding of concatenated codes: Design and analysis of LDPC codes for the erasure channel. Iterative decoding of LDPC codes on the erasure and Gaussian channel. Applications. Fundamentals of transmission theory. Signal theroy. Fourier transform and spectral analysis. Linear Time Invariant systems. Geometrical representation of signals. Baseband amplitude modulated signals. Optimal receivers of noisy signals. Intersymbol interference and Nyquist waveforms. Pass-band signals. Multi-level modulation.
In the written test an exercise solution is required. The exercise is split in a number (4-10) of intermediate goals. At least 25% of the goals must be reached to be admitted to the oral exam. More than 50% of the goals are needed to access the full marking range, otherwise limited to 26/30. The oral exam follows, to test the student competence.
