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TSKS01 Digital Communication

Course program, autumn 2017

Lectures

Please observe that the following lecture plan should be interpreted as an indication about approximately when different topics are treated. The lecture plan will be revised continuously over the course. The lecture slides are published here after each lecture.

Lecture Chapter Main topic Part
1 1 Introduction Course plan, applications, prerequisites.
--- Introduction How do we design efficient digital communication systems?
2 Repetition Basic results from signals and systems
3 Repetition Basic results on stochastic variables and processes. Noise modeling
2 4.1-4.4 Digital modulation AWGN channels and noise modeling. Pulse amplitude modulation and Nyquist criterion. Signals as vectors.
3 4.5-4.8 Digital modulation Geometrical interpretation of signals, representation of white Gaussian noise, examples of basis functions.
5.1-5.3 Detection in AWGN channels Detection of signals disturbed by white gaussian noise. Correlation receivers, matched filter receivers. ML detection.
4 5.4 Detection in AWGN channels Error probability, union bound, nearest-neighbour approximation.
6.1-6.3 Signal constellations Signal constellations: On-off-keying, PSK, FSK, QAM, OFDM. Symbol error probability - bit error probability.
5 5.4.5, 6.4-6.6, 5.5 Detection in AWGN channels Alternative bounds and approximations. Detection of individual bits. Soft detection. Briefly: MAP detection.
6-7 7 Detection in dispersive channels ML sequence estimation, Viterbi algorithm.
8-9 8.1-8.6, 8.8 Error-control coding Error correcting codes, dimension, redundancy, rate. Linear codes, repetition codes, Hamming codes, product codes, cyclic codes, performance.
10 8.7, 8.9, 8.10 Error-control coding Bounds and limits for block codes, basics of CRC codes and convolutional codes
11 10 Practical aspects Eye patterns. Synchronization: Timing recovery and Phase locked loops. Introduction to laboratory exercises
12 9 Link adaptation Link adaptation in packet transmission.

My recommendation is that you read the corresponding part of the course material through once before each lecture and prepare questions. If not, the lecture may not be as useful to you as it could be.

Page responsible: Emil Björnson
Last updated: 2016 11 11   09:46