ECE 611 - Advanced Digital Communications
Instructor
Professor En-Hui Yang
General Description
An introduction to digital communications, entropy, mutual information, rate distortion function, lossless and lossy source coding, communications with AWGN interference, stan- dard modulation techniques, performance analysis, channel capacity and coded modulation, block and convolutional codes including without limitation linear block codes, low density parity check codes, turbo codes, and polar codes, decoding algorithms.
Prerequisites
Probability theory and elementary stochastic processes.
Textbook
J. G. Proakis, Digital Communications, fourth edition, McGraw–Hill, 2001 (Three copies of the fourth edition have been requested to be reserved in the DC library, and the fifth edition is available in UW bookstore); course notes for ECE 611.
Reference
R. G. Gallager, Principles of Digital Communication, Cambridge University Press, 2008.
Course Outline
-
Introduction
(3
hours)
-
Overview
of
digital
communications
- Network of networks
- Bigger pipes, more pipes, and better pipes
- A digital communication block diagram
- Mathematical models for communication channels
- Review of probability theory
- Review of stochastic processes
-
Overview
of
digital
communications
-
Source
Coding
(7
hours)
- Mathematical models for information sources
- Entropy and mutual information
-
Lossless
data
compression
- Coding for discrete sources
- Huffman coding and adaptive Huffman coding
- Run-length coding
- Arithmetic coding
- The context weighting algorithm
- The Lempel-Ziv algorithm
- Grammar-based coding
- Yang-Kieffer algorithms
-
Lossy
data
compression
- Rate distortion function
- Scalar quantization
- Vector quantization
-
Communications
with
AWGN
Interference
(7
hours)
- Vector communications with AWGN interference
- Waveform communications with AWGN interference
- Digital modulation methods
- Probability of Error for various digital modulation methods
- Comparison of digital modulation methods
-
Channel
Capacity
and
Coded
Modulation
(7
hours)
-
Channel
models
and
channel
capacity
- Channel models
- Bandwidth, dimensionality, and channel capacity
- Achieving channel capacity with orthogonal signals in the case of infinite bandwidth
- Channel reliability function
-
Coded
modulation—a
probabilistic
approach
- Random coding based on M-ary binary coded signals
- Random coding based on M-ary multiamplitude signals
- Comparison of R*0 with the capacity of the AWGN channel
-
Channel
models
and
channel
capacity
-
Block
and
Convolutional
Channel
Codes
(12
hours)
-
Linear
Block
codes
- The generator matrix and parity check matrix
- Some specific linear block codes
- Cyclic codes
- Optimum soft decision decoding of linear block codes
- Hard decision decoding
- Comparison of performance between hard decision and soft decision decoding
- Bounds on minimum distance of linear block codes
- Interleaving of coded data for channels with burst errors
- Serial and parallel concatenated block codes
- Low density parity check codes
- Polar codes
-
Convolutional
codes
- The transfer function of a convolutional code
- The Viterbi algorithm
- Probability of error for soft decision decoding
- Probability of error for hard decision decoding
- Distance properties of binary convolutional codes
- Other decoding algorithms for convolutional codes
- Optimal decoding of linear codes for minimizing symbol error rate—the BCJR algorithm
- Parallel and serial concatenated convolutional codes—Turbo codes
-
Linear
Block
codes