• Blended Learning & Flipped Classroom

The goal of the course is to make students familiar with concepts of communication and information theory and with their application to the analysis and design of complex telecommunication systems. Moreover, the course provides students with the tools that are needed to understand  and apply advanced technologies that are presently used in telecommunication systems. Also, understanding, analyzing and designing optical and radio communication systems is one of the objectives of the course.

The first part of the course is finalized to teach students the basics of digital communication as bit rate and symbol rate, bandwidth, filtering of data signals, basics concepts of information theory, modulation and coding. Design of the transmit filter and of the receive filter in practical cases of interest, calculation of the limits of communication in terms of channel capacity, selection of the proper modulation scheme according to the constraints imposed by the system, performance evaluation of modulation and coding schemes are among the goals of the course. 

The second part of the course is finalized to teach students advanced concepts of digital communication as equalization, predistortion, synchronization, MIMO systems. The goal is to make the student familiar with these concepts and with their application to digital transmitters and receivers of radio and optical communication systems. Design of equalization algorithms in the presence of dispersive channels, predistortion algorithms for memoryless non-linear channels, carrier and timing synchronization systems, MIMO receivers are among the goals of the course. Also, learning the mathematical tools needed to understand how these systems work and how their performance can be analyzed is among the objectives of the course. The goal of using commercial software tools as Simulink and Matlab to encode software for the simulation of communication systems will be achieved by laboratory sessions.

Blended learning and flipped classroom will be adopted for about 1/10 of the course to show the relationship between digital communication and other courses in the field of communication and signal processing.

Lectures and exercise sessions of the first part of the course will make students able to

1) Knowledge and understanding. Understand the general principles of data transmission in digital communication systems, analyze and understand the requirements associated to a communication system in terms of signal bandwidth, filtering, modulation, coding.

2)  Applying knowledge and understanding. Identify and define at the block-diagram level a proper filtering, modulation and coding scheme for the most common channel models, compute the performance of  transmission systems in terms of error probability for the most common channel models.

3) Making judgements. Judge if a given filtering,  modulation and coding scheme is appropriate for a practical radio or optical communication system. Recognize the design space and the degrees of freedom that can be exploited to reach given objectives in terms of performance of filtering,  modulation and coding, with specific application to practical radio and optical communication systems.

4) Communication. Communicate to other engineers with presentations and documents about filtering, modulation and coding schemes using the appropriate mathematical tools as Fourier analysis, probability theory, calculus and matrix algebra. Communicate to non-specialists the requirements, the constraints and the features of communication systems using graphical representation (block diagrams) and writings.

Lectures and laboratory sessions of the second part of the course will make students able to

1) Knowledge and understanding. Understand the general principles of advanced topics of digital communication. Analyze and understand the requirements associated to a communication system in terms of equalization, predistortion, synchronization.

2) Applying knowledge and understanding. Identify and define at the block-diagram level a proper equalization, predistortion, synchronization scheme for the most common modulation schemes. Encode software by common commercial tools as Matlab and/or Simulink to simulate the chain of transmitter, channel, receiver of data communication systems.

3) Making judgements. Judge if a given equalization, predistortion, synchronization scheme is appropriate for a practical radio or optical communication system. Recognize the design space and the degrees of freedom that can be exploited to reach given objectives in terms of performance of equalization, predistortion, synchronization, with specific application to practical radio and optical communication systems.

4) Communication. Communicate to engineers in technical documents and presentations about the analysis and design of equalization, predistortion, synchronization, MIMO systems using the appropriate mathematical tools as Fourier analysis, probability theory, calculus and matrix algebra. Communicate to non-engineers by block diagrams and writings about the needs and constraints that advanced telecommunication systems are subject to in terms of receiver performance.

1) First Part

Representation of Signals.  Geometric representation of signals, Schwarz inequality, projection onto the signal space, Gram-Schmidt orthogonalization, geometric representation of noise, signal-to-noise ratio, matched filter. Maximum a Posteriori (MAP) detection and Maximum Likelihood detection in memoryless systems, minimum squared distance detection, correlation detection. Baseband equivalent of passband signals.

Limits of Communication.  Entropy, equivocation, mutual information. Capacity of the discrete-time channel, capacity of the AWGN channel, capacity of the channel with coloured Gaussian noise, water filling.

Modulation. Nyquist filter. QAM and PSK modulation. Error probability, union bound. Law of 6 dB/bit for PSK, law of 3 dB/bit for QAM, back to the AWGN capacity formula. Modulation based on orthogonal signals, examples, performance evaluation. OFDM, cyclic prefix, frequency domain equalization for OFDM.

Binary Channel Codes. Channel capacity with a power constraint, examples of binary block codes, performance of soft and hard decoding. Generator matrix and parity check matrix, generator polynomial and parity check polynomial. Trellis representation of the code and trellis decoding. Convolutional codes, trellis decoding of convolutional codes, performance of convolutional codes. Concatenated coding.

Codes in the signal space. Multidimensional constellations, lattices, lattice partitions.

Exercise activity. Exercises on performance evaluation of communication systems.

 Blended learning and flipped classroom will be adopted for about 1/10 of the course to show the relationship between digital communication and other courses in the field of communication and signal processing.

2) Second Part

Equalization. Wiener’s method. Discrete-time AWGN model, FIR and unconstrained linear equalization, adaptive equalization, decision feedback equalization, channel capacity with decision feedback  equalization. Maximum likelihood sequence detection by the Viterbi algorithm. Maximum a posteriori probability sequence detection by the BCJR algorithm.

Precoding. Tomlinson-Harashima precoding, partial response systems, examples.

Predistortion. AM-AM and AM-PM characteristic of power amplifiers. Adaptive predistortion of memoryless power amplifiers.

Elements of MIMO transmission. The MIMO principle, the MIMO Gaussian channel, detection for the memoryless MIMO channel, detection for the MIMO channel with memory.

Phase-lock loop. Continuous-time PLL, discrete-time PLL, first-order and second-order loop; common phase detectors, design of the loop filter in the presence of phase noise.

Carrier and timing recovery.  Decision-directed phase detector, S-curve, power of M phase detector, differential encoding, feed-forward and feed-back carrier recovery. Square-law timing detector, Gardner detector; digital re-sampling. 

Laboratory activities. Use of Simulink and Matlab for simulation of digital communication systems.

Blended learning and flipped classroom will be adopted for about 1/10 of the course to show the relationship between digital communication and other courses in the field of communication and signal processing. 

Knowledge of Signal Theory: Fourier analysis, filtering, basics of digital signal processing.

The exam of the first part can be taken in the mid-term session, at the end of October or at the beginning of November, or in any one of the July, September, February sessions. The exam consists in a written part based on numerical exercises, and in an oral part based on the theory and on the written exam. The student will be asked to analyze communication systems in terms of bit rate, symbol rate, bandwidth and filtering requirements, to draw block diagrams of digital communication systems and to explain them by writings and equations, to compute system performance in terms of error probability, to compute other main system physical parameters as energy, signal-to-noise ratio, power.

The exam of the second part can be taken in any one of the July, September, February sessions. The exam consists in a written part based on a question about the theory, in an oral part based on the theory and on the written exam. The oral part can include an optional (up to the student) presentation of the laboratory activities.The student will be asked to answer questions regarding the theory, to draw block diagrams of digital communication systems and to explain them by writings and equations, to compute system performance in terms of error probability and/or mean square error, to explain and comment on the code encoded during the laboratory activity (optional).