The student will learn some of the advanced techniques for digital transmission and coding of the information. Accordingly, he/she will be able to design the basic elements of a modern digital communication system, explaining the rational behind his/her choices.
Course Prerequisites
The course is aimed at students with a basic knowledge of electrical communication and signal theory.
Teaching Methods
The course is structured into lectures and practical classes. The concepts are introduced by means of lectures with slides integrated with explanation at the blackboard. Complementary topics are presented by means of one or two seminars by company representatives introducing examples of real digital communication systems.
Assessment Methods
Oral test, with questions aiming at understanding which are the concepts acquired by the student and his/her ability to explain how the functional blocks of digital systems work. As an alternative to the oral exam, a project may be submitted, whose topic must be defined together with the course instructor. The project may include: analysis and understanding of the bibliographic references, description of the architectures of the system under study, and parts of the project possibly implemented in Matlab or through other tools used in class. In both cases, the evaluation will focus on the ability to understand the fundamental principles of the subject and to apply the acquired techniques to concrete case studies. The minimum passing grade is 18, while the maximum grade is 30 with honors.
Texts
J.R. Barry, E.A. Lee, D.G. Messerschmitt. Digital Communication (third edition). Springer 2004
Contents
The course is for students with a basic knowledge of transmission techniques and is devoted to digital communications Course introductory notes Stochastic variables and processes Information Theory: entropy Information Theory: source coding Channel capacity Coding techniques for information protection Codes for error detection and correction Algebraic codes Convolutional codes, maximum likelihood decoding, Viterbi algorithm Concatenated codes Turbo codes LDPC codes Transmission on AWGN channels Digital signals: PSD and power Nyquist criterion to avoid intersymbol interference (ISI) Optimum decoder Upper and Lower bounds for BER values Channels with fading Fading definition and effects Diversity techniques Linear equalizers: Zero Forcing Equalizers Linear equalizers: LMS, fractional and Decision Feedback equalizers. Introduction to ML equalizers. Synchronization and syntonization Frequency error estimates (open-loop and closed-loop) Phase error estimates (open-loop and closed-loop) Timing error estimates (open-loop and closed-loop)
