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SSY125 Digital communications lp2 HT20 (7.5 hp)

Course is offered by the department of Electrical Engineering

The course will be given online (Zoom).

Course purpose

This course introduces the basics of information and coding theory. We will be concerned with the fundamental communication problem of sending information from a transmitter (source) to a receiver over a physical channel efficiently and reliably. 

Several questions immediately come to mind when reading the above paragraph. What is meant by information? How can we represent efficiently (i.e., compress) a source? How is the transmission cost calculated? How is reliability defined and measured? How much information can be transmitted reliably over the channel? What design tradeoffs can be made? The aim of this course is to answer these questions. 

 

Learning outcomes

After completion of this course, the student should be able to

  • Define entropy and mutual information and explain their operational meaning.
  • Describe Shannon's source coding and channel coding theorems.
  • Apply Huffmann codes to compress discrete memoryless sources losslessly.
  • Compute the capacity of discrete memoryless point-to-point channels.
  • Describe Shannon's capacity formula on the additive white Gaussian noise (AWGN) channel and elaborate on the fundamental tradeoff between power and bandwidth.
  • Compute and estimate the symbol and bit error probability of simple modulations (PAM, PSK, QAM) for transmission over the AWGN channel. 
  • Estimate the performance of communication links (i.e., modulation formats, channel codes and decoders) over the AWGN channel by computer simulation. This includes determining simulation parameters to reach the desired accuracy as well as programming the simulation in MATLAB. 

 

Content

  • From continuous-time to discrete-time AWGN channel
  • Entropy, data compression, prefix-free codes, Kraft inequality, Huffman codes, the source coding theorem.
  • Mutual information, channel capacity, the channel coding theorem.
  • Detection theory: maximum likelihood (ML) and maximum a posteriori detection.
  • Methods for computing and bounding the symbol and bit error probabilities: decision regions, Q-function, union bound techniques.
  • Analysis of linear modulation formats (PAM, PSK, QAM), power and spectral efficiency.
  • Channel coding, Hamming distance, hard- and soft-decision decoding.
  • Linear binary block codes: generator and parity-check matrices, syndrome decoding, error correction and error detection capability.
  • Binary convolutional codes: trellis diagram, ML decoding, Viterbi algorithm, union bound on the error probability for ML soft- and hard-decision decoding.
  • Modern codes: turbo codes and low-density parity-check codes, iterative message-passing decoding.

 

Course staff

  • Examiner and Lecturer: Alexandre Graell i Amat (alexandre.graell@chalmers.se). Office: Room 6409, Department of Electrical Engineering
  • Teaching assistants: Shen Li (shenl@chalmers.se, office: 6439) and Mohammad Farsi (farsim@chalmers.se, office: 6340)

 

Course literature

This course does not have any mandatory literature. The course is based on a complete set of lecture notes that will be made available in Canvas and therefore it does not follow a particular textbook. 

For those students who wish to delve deeper the following books may be interesting:

  • Stefan M. Moser and Po-Ning Chen, A Student's Guide to Coding and Information Theory, Cambridge University Press, 2012.
  • W. E. Ryan and S. Lin, Channel codes. Classical and modern, Cambridge: Cambridge University Press, 2009.
  • John Proakis, Digital Communications, McGraw-Hill, 2000.
  • Sergio Benedetto, Ezio Biglieri, Principles of Digital Transmission: With Wireless Applications, Kluwer Academic Publishers, 1999;

Additional course material

Slides for the lectures, exercises for the tutorial sessions, and additional material will be made available on the course web page.

 

Schedule

Please see the course memo.

Also, please check the calendar in Canvas

 

Zoom link

Lectures: Join from PC, Mac, Linux, iOS, or Android: https://chalmers.zoom.us/j/67292734875 (Links to an external site.)
Password: 569838

Tutorial on Monday: https://chalmers.zoom.us/j/8653909537

Tutorial on Wednesday:  https://chalmers.zoom.us/j/2074909656

Grading

The final grade on the course will be decided by the project (maximum score 30), quizzes (maximum score 3, rounded to the nearest integer), tutorials (maximum score 7), and final exam (maximum score 60). The sum of all scores will decide the grade according to the following table. A minimum score of 15 points in the exam is required to pass the course. Furthermore, the project must be approved to receive a passing grade.

Detailed information is provided in the course memo.

 

Course summary:

Date Details Due