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6.451 Principles of Digital Communication II, Spring 2005

by: OCW

Publication date: 2005

Usage: Attribution-NonCommercial-ShareAlike 4.0 International

Language: English

Instructor: Prof. David Forney

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.

* NOTE: The Real Media files are the originals. The MP4s are transcoded versions for the iPod.

Visit the course on MIT OpenCourseWare: http://ocw.mit.edu/6-451S05
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Addeddate: 2009-04-25 11:12:39

Color: color

Comment: tracey - this item may have been damaged on 2009-04-28 when 2 catalogd main processes ran simultaneously and is checking item

Identifier: MIT6.451S05

Sound: sound

Year: 2005

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ITEM TILE

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158.9M

Lecture 10: Reed-Solomon Codes

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Lecture 11: Reed-Solomon Codes

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Lecture 12: Reed-Solomon Codes

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Lecture 13: Introduction to Convolutional Codes

157.0M

Lecture 14: Introduction to Convolutional Codes

154.4M

Lecture 15: Trellis Representations of Binary Linear Block Codes

157.3M

Lecture 16: Trellis Representations of Binary Linear Block Codes

153.7M

Lecture 17: Codes on Graphs

158.6M

Lecture 18: Codes on Graphs

156.6M

Lecture 19: The Sum-Product Algorithm

151.4M

Lecture 1: Introduction Sampling Theorem

150.1M

Lecture 20: Turbo, LDPC, and RA Codes

149.0M

Lecture 21: Turbo, LDPC, and RA Codes

151.9M

Lecture 22: Lattice and Trellis Codes

129.4M

Lecture 23: Lattice and Trellis Codes

155.0M

Lecture 24: Linear Gaussian Channels

161.5M

Lecture 25: Linear Gaussian Channels

145.2M

Lecture 2: Performance of Small Signal Constellations

157.4M

Lecture 3: Hard-decision and Soft-decision Decoding

144.2M

Lecture 4: Hard-decision and Soft-decision Decoding

179.2M

Lecture 5: Introduction to Binary Block Codes

155.7M

Lecture 6: Introduction to Binary Block Codes

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Lecture 7: Introduction to Finite Fields

161.7M

Lecture 8: Introduction to Finite Fields

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Lecture 9: Introduction to Finite Fields

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Lecture 10: Reed-Solomon Codes

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Lecture 11: Reed-Solomon Codes

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Lecture 12: Reed-Solomon Codes

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Lecture 13: Introduction to Convolutional Codes

41.0M

Lecture 14: Introduction to Convolutional Codes

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Lecture 15: Trellis Representations of Binary Linear Block Codes

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Lecture 16: Trellis Representations of Binary Linear Block Codes

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Lecture 17: Codes on Graphs

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Lecture 18: Codes on Graphs

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Lecture 19: The Sum-Product Algorithm

38.6M

Lecture 1: Introduction Sampling Theorem

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Lecture 20: Turbo, LDPC, and RA Codes

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Lecture 21: Turbo, LDPC, and RA Codes

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Lecture 22: Lattice and Trellis Codes

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Lecture 23: Lattice and Trellis Codes

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Lecture 24: Linear Gaussian Channels

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Lecture 25: Linear Gaussian Channels

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Lecture 2: Performance of Small Signal Constellations

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Lecture 3: Hard-decision and Soft-decision Decoding

34.9M

Lecture 4: Hard-decision and Soft-decision Decoding

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Lecture 5: Introduction to Binary Block Codes

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Lecture 6: Introduction to Binary Block Codes

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Lecture 7: Introduction to Finite Fields

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Lecture 8: Introduction to Finite Fields

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Lecture 9: Introduction to Finite Fields

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Lecture 1: Introduction Sampling Theorem (Real Media)

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Lecture 9: Introduction to Finite Fields (Real Media)

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Lecture 22: Lattice and Trellis Codes (Real Media)

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Lecture 15: Trellis Representations of Binary Linear Block Codes (Real Media)

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Lecture 23: Lattice and Trellis Codes (Real Media)

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Lecture 16: Trellis Representations of Binary Linear Block Codes (Real Media)

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Lecture 2: Performance of Small Signal Constellations (Real Media)

132.7M

Lecture 10: Reed-Solomon Codes (Real Media)

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Lecture 3: Hard-decision and Soft-decision Decoding (Real Media)

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Lecture 11: Reed-Solomon Codes (Real Media)

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Lecture 24: Linear Gaussian Channels (Real Media)

146.9M

Lecture 17: Codes on Graphs (Real Media)

154.4M

Lecture 25: Linear Gaussian Channels (Real Media)

151.7M

Lecture 18: Codes on Graphs (Real Media)

120.4M

Lecture 4: Hard-decision and Soft-decision Decoding (Real Media)

146.6M

Lecture 12: Reed-Solomon Codes (Real Media)

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Lecture 5: Introduction to Binary Block Codes (Real Media)

149.8M

Lecture 19: The Sum-Product Algorithm (Real Media)

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Lecture 6: Introduction to Binary Block Codes (Real Media)

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Lecture 7: Introduction to Finite Fields (Real Media)

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Lecture 20: Turbo, LDPC, and RA Codes (Real Media)

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Lecture 21: Turbo, LDPC, and RA Codes (Real Media)

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Lecture 8: Introduction to Finite Fields (Real Media)

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Lecture 13: Introduction to Convolutional Codes (Real Media)

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Lecture 12: Reed-Solomon Codes

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Lecture 13: Introduction to Convolutional Codes

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Lecture 14: Introduction to Convolutional Codes

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Lecture 15: Trellis Representations of Binary Linear Block Codes

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Lecture 16: Trellis Representations of Binary Linear Block Codes

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Lecture 17: Codes on Graphs

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Lecture 18: Codes on Graphs

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Lecture 19: The Sum-Product Algorithm

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Lecture 20: Turbo, LDPC, and RA Codes

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Lecture 21: Turbo, LDPC, and RA Codes

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Lecture 22: Lattice and Trellis Codes

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Lecture 23: Lattice and Trellis Codes

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Lecture 24: Linear Gaussian Channels

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Lecture 25: Linear Gaussian Channels

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Lecture 2: Performance of Small Signal Constellations

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Lecture 3: Hard-decision and Soft-decision Decoding

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Lecture 4: Hard-decision and Soft-decision Decoding

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Lecture 5: Introduction to Binary Block Codes

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Lecture 6: Introduction to Binary Block Codes

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Lecture 7: Introduction to Finite Fields

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Lecture 8: Introduction to Finite Fields

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