Instructor

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Instructor

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Overview

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.

34 hours of lectures.

25 lectures.

Assignments and exams with solutions.

Reference Texts

Instructor(s)

G. David Forney, Jr. received the B.S.E. degree in electrical engineering from Princeton University, Princeton, NJ, in 1961, and the M.S. and Sc.D. degrees in electrical engineering from the Massachusetts Institute of Technology, Cambridge, MA, in 1963 and 1965, respectively. From 1965-99 he was with the Codex Corporation, which was acquired by Motorola, Inc. in 1977, and its successor, the Motorola Information Systems Group, Mansfield, MA.  Since 1996, he has been an Adjunct Professor at M.I.T.

Source: IEEE Information page

 


Principles of Digital Communication II by Prof. David Forney is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-451-principles-of-digital-communication-ii-spring-2005/.

Course content

  • 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

  • Assignments

  • Exams

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