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Syllabus for

Academic year
SSY125 - Digital communications
Syllabus adopted 2013-02-14 by Head of Programme (or corresponding)
Owner: MPCOM
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Electrical Engineering

Teaching language: English
Open for exchange students
Block schedule: A

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0107 Examination 7,5 c Grading: TH   7,5 c   16 Dec 2013 pm M,  23 Apr 2014 am V,  25 Aug 2014 pm V

In programs



Bitr professor  Alexandre Graell i Amat


ESS140   Digital communications ESS195   Digital communications

Course evaluation:

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For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

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.

Learning outcomes (after completion of the course the student should be able to)

  • 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

  • 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

  • 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, 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, equalization (ZF, MMSE)


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.

Page manager Published: Mon 28 Nov 2016.