@article{,
title= {MIT OCW 6.451 Principles of Digital Communication II Spring 05},
keywords= {MIT6.451S05},
journal= {Massachusetts Institute of Technology: MIT OpenCourseWare},
author= {David Forney},
year= {2005},
url= {http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-451-principles-of-digital-communication-ii-spring-2005/},
license= {BY-NC-SA},
abstract= {==Course Description
This course is the second of a two-term sequence with 6.450. The focus is on coding techniques for approaching the Shannon limit of additive white Gaussian noise (AWGN) channels, their performance analysis, and design principles. After a review of 6.450 and the Shannon limit for AWGN channels, the course begins by discussing small signal constellations, performance analysis and coding gain, and hard-decision and soft-decision decoding. It continues with binary linear block codes, Reed-Muller codes, finite fields, Reed-Solomon and BCH codes, binary linear convolutional codes, and the Viterbi algorithm.

More advanced topics include trellis representations of binary linear block codes and trellis-based decoding; codes on graphs; the sum-product and min-sum algorithms; the BCJR algorithm; turbo codes, LDPC codes and RA codes; and performance of LDPC codes with iterative decoding. Finally, the course addresses coding for the bandwidth-limited regime, including lattice codes, trellis-coded modulation, multilevel coding and shaping. If time permits, it covers equalization of linear Gaussian channels.

Lecture 1: Introduction Sampling Theorem
Lecture 2: Performance of Small Signal Constellations
Lecture 3: Hard-decision and Soft-decision Decoding
Lecture 4: Hard-decision and Soft-decision Decoding
Lecture 5: Introduction to Binary Block Codes
Lecture 6: Introduction to Binary Block Codes
Lecture 7: Introduction to Finite Fields
Lecture 8: Introduction to Finite Fields
Lecture 9: Introduction to Finite Fields
Lecture 10: Reed-Solomon Codes
Lecture 11: Reed-Solomon Codes
Lecture 12: Reed-Solomon Codes
Lecture 13: Introduction to Convolutional Codes
Lecture 14: Introduction to Convolutional Codes
Lecture 15: Trellis Representations of Binary Linear Block Codes
Lecture 16: Trellis Representations of Binary Linear Block Codes
Lecture 17: Codes on Graphs
Lecture 18: Codes on Graphs
Lecture 19: The Sum-Product Algorithm
Lecture 20: Turbo, LDPC, and RA Codes
Lecture 21: Turbo, LDPC, and RA Codes
Lecture 22: Lattice and Trellis Codes
Lecture 23: Lattice and Trellis Codes
Lecture 24: Linear Gaussian Channels
Lecture 25: Linear Gaussian Channels


},
tos= {},
superseded= {},
terms= {}
}