Credit points: 3.5

Prerequisite: Random Signals & Noise.

The course is taught in English.

Structure of Digital Communication systems. Hypothesis testing and decision rules: Minimum error probability, maximum likelihood, Bayes, Neyman-Pearson. Discrete-time Multidimensional communication systems (vector channels), continuous-time communication systems (waveform channels). Signal-space representation of finite-energy signals. The optimum detector for known signals in additive white Gaussian noise channels (AWGN) and colored additive Gaussian noise channels. Bit error probability and performance analysis of digital communication systems. Digital modulation techniques: PSK, FSK, MSK, orthogonal signals, simplex signals. Non-coherent communications: The optimum detector for known signals with unknown phase in an AWGN channel. Coding theory: Block codes, convolutional codes, the Viterbi algorithm, Trellis Coded Modulation (TCM). Introduction to information theory and channel capacity.  

Credit points: 3.5

Prerequisite: Random Signals & Noise.

The course is taught in English.

Structure of Digital Communication systems. Hypothesis testing and decision rules: Minimum error probability, maximum likelihood, Bayes, Neyman-Pearson. Discrete-time Multidimensional communication systems (vector channels), continuous-time communication systems (waveform channels). Signal-space representation of finite-energy signals. The optimum detector for known signals in additive white Gaussian noise channels (AWGN) and colored additive Gaussian noise channels. Bit error probability and performance analysis of digital communication systems. Digital modulation techniques: PSK, FSK, MSK, orthogonal signals, simplex signals. Non-coherent communications: The optimum detector for known signals with unknown phase in an AWGN channel. Coding theory: Block codes, convolutional codes, the Viterbi algorithm, Trellis Coded Modulation (TCM). Introduction to information theory and channel capacity.  

Credit points: 3.5

Prerequisite: Random Signals & Noise.

The course is taught in English.

Structure of Digital Communication systems. Hypothesis testing and decision rules: Minimum error probability, maximum likelihood, Bayes, Neyman-Pearson. Discrete-time Multidimensional communication systems (vector channels), continuous-time communication systems (waveform channels). Signal-space representation of finite-energy signals. The optimum detector for known signals in additive white Gaussian noise channels (AWGN) and colored additive Gaussian noise channels. Bit error probability and performance analysis of digital communication systems. Digital modulation techniques: PSK, FSK, MSK, orthogonal signals, simplex signals. Non-coherent communications: The optimum detector for known signals with unknown phase in an AWGN channel. Coding theory: Block codes, convolutional codes, the Viterbi algorithm, Trellis Coded Modulation (TCM). Introduction to information theory and channel capacity.