The course provides the student with specific knowledge about the statistical methods to analyze the performance of simple systems and telecommunications networks. The student will gain basic knowledge of signal and image processing for multimedia applications. The course will teach the student how to connect the different blocks of a telecommunications system in a single set of integrated and interdependent processes. Moreover, it will provide an overview of the major telecommunications systems, and will briefly describe the fundamental concepts of transmission in mobile channels.
Curriculum
teacher profile teaching materials
Architecture of a communication system. Continuous and discrete signals: step and Dirac impulse signals, complex exponentials; elementary operations. Energy, power, periodicity of continuous and discrete signals; power of periodic signals. Linear, time-invariant, and causal systems. The impulse response. The Fourier series and its properties. Parseval theorem for periodic signals. The convolution and correlation of continue and discrete signals.
• Signals representation in the frequency domain
Fourier transform of continuous signals and its properties: linearity, time shift, frequency shift (modulation), product, duality, scale change, derivation, integration, convolution and correlation. Spectral energy density. Spectrum of periodic signals. Sampling theorem. Aliasing, energy and power of sampled signals. Reconstruction approaches of sampled signals. Fourier transform of discrete signals and its properties.
• Random signals
Basic concepts. Frequentist and axiomatic description. Continuous and discrete random variables. Cumulative distribution function, probability density (mass) function, characteristic function. Independent random variables. Joint, marginal and conditional probability density functions. Law of total probability. Bayes’ theorem. Gaussian, uniform, binomial, one-sides exponential statistics. Statistical moments of random variables: mean (expected value), variance, root mean square and their relations. Uncorrelated and statistically independent random variables. Functions of random variables and their probability density functions. Probability density function of the sum and the linear combination of independent random variables. Random processes and their statistics. Correlation and covariance. Stationary and ergodic processes. The harmonic process. The additive white Gaussian noise (AWGN). Random process through a system.
• Information theory and source coding
Basics of the information theory, self-information and entropy. Quantization. First Shannon theorem. Huffman coding.
• Baseband digital transmission
Binary and multi-level line encoding. Pulse amplitude modulation (PAM) and pulse coded modulation (PCM). Inter-symbol interference (ISI) and Nyquist theorem, Nyquist pulses. Noise and error probability in binary and multilevel PAM transmission. Matched filter and corresponding error probability.
• Passband digital transmission
Amplitude shift keying (ASK), quadrature amplitude modulation (QAM) and phase shift keying (PSK). Transmitter and receiver scheme. Constellations and distance between symbols. Symbol energy.
• Channel capacity and encoding
Second Shannon theorem. Channel capacity and encoding. Hard decoding and Hamming distance.
Claudio Prati, "Segnali e Sistemi per le Telecomunicazioni", seconda edizione, McGraw-Hill, 2010.
Programme
• Continuous and discrete signals and systemsArchitecture of a communication system. Continuous and discrete signals: step and Dirac impulse signals, complex exponentials; elementary operations. Energy, power, periodicity of continuous and discrete signals; power of periodic signals. Linear, time-invariant, and causal systems. The impulse response. The Fourier series and its properties. Parseval theorem for periodic signals. The convolution and correlation of continue and discrete signals.
• Signals representation in the frequency domain
Fourier transform of continuous signals and its properties: linearity, time shift, frequency shift (modulation), product, duality, scale change, derivation, integration, convolution and correlation. Spectral energy density. Spectrum of periodic signals. Sampling theorem. Aliasing, energy and power of sampled signals. Reconstruction approaches of sampled signals. Fourier transform of discrete signals and its properties.
• Random signals
Basic concepts. Frequentist and axiomatic description. Continuous and discrete random variables. Cumulative distribution function, probability density (mass) function, characteristic function. Independent random variables. Joint, marginal and conditional probability density functions. Law of total probability. Bayes’ theorem. Gaussian, uniform, binomial, one-sides exponential statistics. Statistical moments of random variables: mean (expected value), variance, root mean square and their relations. Uncorrelated and statistically independent random variables. Functions of random variables and their probability density functions. Probability density function of the sum and the linear combination of independent random variables. Random processes and their statistics. Correlation and covariance. Stationary and ergodic processes. The harmonic process. The additive white Gaussian noise (AWGN). Random process through a system.
• Information theory and source coding
Basics of the information theory, self-information and entropy. Quantization. First Shannon theorem. Huffman coding.
• Baseband digital transmission
Binary and multi-level line encoding. Pulse amplitude modulation (PAM) and pulse coded modulation (PCM). Inter-symbol interference (ISI) and Nyquist theorem, Nyquist pulses. Noise and error probability in binary and multilevel PAM transmission. Matched filter and corresponding error probability.
• Passband digital transmission
Amplitude shift keying (ASK), quadrature amplitude modulation (QAM) and phase shift keying (PSK). Transmitter and receiver scheme. Constellations and distance between symbols. Symbol energy.
• Channel capacity and encoding
Second Shannon theorem. Channel capacity and encoding. Hard decoding and Hamming distance.
Core Documentation
A.B. Carlson, P.B. Crilly, J.C. Rutledge, "Communication Systems: an introduction to signals and noise in electrical communication", McGraw-Hill international Edition publ.Claudio Prati, "Segnali e Sistemi per le Telecomunicazioni", seconda edizione, McGraw-Hill, 2010.
Reference Bibliography
Testi di Riferimento A.B. Carlson, P.B. Crilly, J.C. Rutledge, "Communication Systems: an introduction to signals and noise in electrical communication", McGraw-Hill international Edition publ. Claudio Prati, "Segnali e Sistemi per le Telecomunicazioni", seconda edizione, McGraw-Hill, 2010.Type of delivery of the course
lessons are given in a classroomAttendance
no mandatory attendance to lessonsType of evaluation
written and oral test teacher profile teaching materials
Architecture of a communication system. Continuous and discrete signals: step and Dirac impulse signals, complex exponentials; elementary operations. Energy, power, periodicity of continuous and discrete signals; power of periodic signals. Linear, time-invariant, and causal systems. The impulse response. The Fourier series and its properties. Parseval theorem for periodic signals. The convolution and correlation of continue and discrete signals.
• Signals representation in the frequency domain
Fourier transform of continuous signals and its properties: linearity, time shift, frequency shift (modulation), product, duality, scale change, derivation, integration, convolution and correlation. Spectral energy density. Spectrum of periodic signals. Sampling theorem. Aliasing, energy and power of sampled signals. Reconstruction approaches of sampled signals. Fourier transform of discrete signals and its properties.
• Random signals
Basic concepts. Frequentist and axiomatic description. Continuous and discrete random variables. Cumulative distribution function, probability density (mass) function, characteristic function. Independent random variables. Joint, marginal and conditional probability density functions. Law of total probability. Bayes’ theorem. Gaussian, uniform, binomial, one-sides exponential statistics. Statistical moments of random variables: mean (expected value), variance, root mean square and their relations. Uncorrelated and statistically independent random variables. Functions of random variables and their probability density functions. Probability density function of the sum and the linear combination of independent random variables. Random processes and their statistics. Correlation and covariance. Stationary and ergodic processes. The harmonic process. The additive white Gaussian noise (AWGN). Random process through a system.
• Information theory and source coding
Basics of the information theory, self-information and entropy. Quantization. First Shannon theorem. Huffman coding.
• Baseband digital transmission
Binary and multi-level line encoding. Pulse amplitude modulation (PAM) and pulse coded modulation (PCM). Inter-symbol interference (ISI) and Nyquist theorem, Nyquist pulses. Noise and error probability in binary and multilevel PAM transmission. Matched filter and corresponding error probability.
• Passband digital transmission
Amplitude shift keying (ASK), quadrature amplitude modulation (QAM) and phase shift keying (PSK). Transmitter and receiver scheme. Constellations and distance between symbols. Symbol energy.
• Channel capacity and encoding
Second Shannon theorem. Channel capacity and encoding. Hard decoding and Hamming distance.
Claudio Prati, "Segnali e Sistemi per le Telecomunicazioni", seconda edizione, McGraw-Hill, 2010.
Mutuazione: 20801776 FONDAMENTI DI TELECOMUNICAZIONI in Ingegneria informatica L-8 CINCOTTI GABRIELLA
Programme
• Continuous and discrete signals and systemsArchitecture of a communication system. Continuous and discrete signals: step and Dirac impulse signals, complex exponentials; elementary operations. Energy, power, periodicity of continuous and discrete signals; power of periodic signals. Linear, time-invariant, and causal systems. The impulse response. The Fourier series and its properties. Parseval theorem for periodic signals. The convolution and correlation of continue and discrete signals.
• Signals representation in the frequency domain
Fourier transform of continuous signals and its properties: linearity, time shift, frequency shift (modulation), product, duality, scale change, derivation, integration, convolution and correlation. Spectral energy density. Spectrum of periodic signals. Sampling theorem. Aliasing, energy and power of sampled signals. Reconstruction approaches of sampled signals. Fourier transform of discrete signals and its properties.
• Random signals
Basic concepts. Frequentist and axiomatic description. Continuous and discrete random variables. Cumulative distribution function, probability density (mass) function, characteristic function. Independent random variables. Joint, marginal and conditional probability density functions. Law of total probability. Bayes’ theorem. Gaussian, uniform, binomial, one-sides exponential statistics. Statistical moments of random variables: mean (expected value), variance, root mean square and their relations. Uncorrelated and statistically independent random variables. Functions of random variables and their probability density functions. Probability density function of the sum and the linear combination of independent random variables. Random processes and their statistics. Correlation and covariance. Stationary and ergodic processes. The harmonic process. The additive white Gaussian noise (AWGN). Random process through a system.
• Information theory and source coding
Basics of the information theory, self-information and entropy. Quantization. First Shannon theorem. Huffman coding.
• Baseband digital transmission
Binary and multi-level line encoding. Pulse amplitude modulation (PAM) and pulse coded modulation (PCM). Inter-symbol interference (ISI) and Nyquist theorem, Nyquist pulses. Noise and error probability in binary and multilevel PAM transmission. Matched filter and corresponding error probability.
• Passband digital transmission
Amplitude shift keying (ASK), quadrature amplitude modulation (QAM) and phase shift keying (PSK). Transmitter and receiver scheme. Constellations and distance between symbols. Symbol energy.
• Channel capacity and encoding
Second Shannon theorem. Channel capacity and encoding. Hard decoding and Hamming distance.
Core Documentation
A.B. Carlson, P.B. Crilly, J.C. Rutledge, "Communication Systems: an introduction to signals and noise in electrical communication", McGraw-Hill international Edition publ.Claudio Prati, "Segnali e Sistemi per le Telecomunicazioni", seconda edizione, McGraw-Hill, 2010.
Reference Bibliography
Testi di Riferimento A.B. Carlson, P.B. Crilly, J.C. Rutledge, "Communication Systems: an introduction to signals and noise in electrical communication", McGraw-Hill international Edition publ. Claudio Prati, "Segnali e Sistemi per le Telecomunicazioni", seconda edizione, McGraw-Hill, 2010.Type of delivery of the course
lessons are given in a classroomAttendance
no mandatory attendance to lessonsType of evaluation
written and oral test