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Institutionernas kurser för doktorander


Kursplan för

SSY125 - Digital communications
Kursplanen fastställd 2012-02-21 av programansvarig (eller motsvarande)
Ägare: MPCOM
7,5 Poäng
Betygskala: TH - Fem, Fyra, Tre, Underkänt
Utbildningsnivå: Avancerad nivå
Huvudområde: Elektroteknik
Institution: 32 - ELEKTROTEKNIK

Undervisningsspråk: Engelska
Sökbar för utbytesstudenter
Blockschema: A

Modul   Poängfördelning   Tentamensdatum
Lp1 Lp2 Lp3 Lp4 Sommarkurs Ej Lp
0107 Tentamen 7,5hp Betygskala: TH   7,5hp   17 Dec 2012 em H,  03 Apr 2013 fm V,  26 Aug 2013 em V

I program



Bitr professor  Alexandre Graell i Amat


ESS140   Digital communications ESS195   Digital kommunikation F




För kurser inom Chalmers utbildningsprogram gäller samma behörighetskrav som till de(t) program kursen ingår i.

Kursspecifika förkunskaper

A passing grade in SSY121 Introduction to Communication Engineering, or a similar course, is required. Working knowledge of probability theory and signals and systems (especially transforms, filtering, convolution, sampling theorem) and experience of MATLAB is required. Knowledge of random processes is very useful, but not essential. Hence, the course Random signals analysis is recommended.


In this course, we will be concerned with the design of a system that transfers information from point A over a physical channel to point B. Of course, we would like to do this at the lowest possible cost, but at the same time we must ensure that the quality of the information transfer is acceptable.
Several questions immediately come to mind when reading the above paragraph. What is meant by information? How is the cost calculated? How is quality defined and measured? What design trade-offs can be made?
The aim of this course is to answer these questions.

Lärandemål (efter fullgjord kurs ska studenten kunna)

  • define and interpret the a priori and a posterior probability density functions for the transmitted bits, and explain how the a posterior distribution depends on the noise distribution for additive white Gaussian noise channels

  • describe how a discrete-time vector model can model a continuous-time waveform channel

  • elaborate on the fundamental trade-off between transmitted signals; power and bandwidth in order to reach a certain error performance of the communication link (Shannon's channel capacity and spectral and power efficiency are key concepts here)

  • compute or estimate the symbol and bit error probability for transmission over linear additive white Gaussian noise channels for several modulation methods (e.g., PAM, PSK, QAM, FSK)

  • estimate the performance of communication links (i.e., modulation formats, channel codes and decoders, and equalizers) over linear additive white Gaussian noise channels by computer simulations. This includes determining simulation parameters to reach the desired accuracy as well as programming the simulation in MATLAB.

  • describe and compare complexity and performance of the following channel equalizations methods: zero-forcing, linear MMSE, and decision feedback

  • explain the advantages and disadvantages of block and convolutional channel coding, define and compare some major decoding methods (syndrome, Viterbi), and estimate the error performance for channel-coded systems

  • design communication links (modulation, channel coding, and receiver algorithms) for linear additive white Gaussian channels such that specified requirements on power and spectral efficiency are satisfied.


  • Review of signal space concepts: orthogonal waveforms, orthonormal waveforms, inner product, norms, bases.

  • Review of signal constellations: antipodal, orthogonal, simplex

  • Detection theory: maximum likelihood and maximum a posteriori detection

  • Methods for computing and bounding symbol and bit error probabilities: decision regions, Q-function, union bound techniques

  • Error analysis of common modulation formats: PAM, PSK, QAM, FSK

  • Power spectrum and spectral efficiency

  • Channel capacity for the Gaussian channel

  • Linear binary block codes: generator and parity check matrices, standard array and syndrome decoding, error correcting capability, error detecting capability, union bound for soft and hard ML decoding

  • Binary convolutional codes: state diagram, trellis, ML decoding, Viterbi algorithm, union bound on bit error probability for soft and hard ML decoding

  • Maximum-likelihood sequence detection

  • Bandlimited channels: ISI, Nyquist pulse shaping, raised-cosine pulses, equalization (ZF, MMSE, DFE)


The course is comprised of approximately 16 lectures, 12 exercise sessions, 3 quizzes, and 1 project.


Upamanyu Madhow, Fundamentals of Digital Communication, Cambridge University Press, 2008, ISBN-10: 0521874149, ISBN-13: 978-0521874144


The final grade (TH) is based on scores from projects, quizzes, and a written exam. The project and the literature study are mandatory in the sense that they must be passed to pass the course.

Sidansvarig Publicerad: må 13 jul 2020.