6.451 Principles of Digital Communication II, Spring 2003
Coding for the AWGN channel; block and convolutional codes; lattice and trellis codes; capacity-approaching codes; equalization of linear Gaussian channels; linear, decision-feedback, and MLSD equalization; precoding; multicarrier modulation; and topics in wireless communication. Description from th...
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| Format: | Learning Object |
| Language: | en-US |
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2003
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| Online Access: | http://hdl.handle.net/1721.1/36834 |
| _version_ | 1826217409571192832 |
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| author | Forney, G. David |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Forney, G. David |
| author_sort | Forney, G. David |
| collection | MIT |
| description | Coding for the AWGN channel; block and convolutional codes; lattice and trellis codes; capacity-approaching codes; equalization of linear Gaussian channels; linear, decision-feedback, and MLSD equalization; precoding; multicarrier modulation; and topics in wireless communication. Description from the course home page: 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. |
| first_indexed | 2024-09-23T17:03:12Z |
| format | Learning Object |
| id | mit-1721.1/36834 |
| institution | Massachusetts Institute of Technology |
| language | en-US |
| last_indexed | 2025-03-10T14:23:59Z |
| publishDate | 2003 |
| record_format | dspace |
| spelling | mit-1721.1/368342025-02-25T16:49:39Z 6.451 Principles of Digital Communication II, Spring 2003 Principles of Digital Communication II Forney, G. David Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science coding techniques the Shannon limit of additive white Gaussian noise channels Small signal constellations performance analysis coding gain Hard-decision and soft-decision decoding Introduction to binary linear block codes Reed-Muller codes finite fields Reed-Solomon and BCH codes binary linear convolutional codes Viterbi and BCJR algorithms Trellis representations of binary linear block codes trellis-based ML decoding Codes on graphs sum-product max-product decoding algorithms Turbo codes LDPC codes and RA codes Coding for the bandwidth-limited regime Lattice codes Trellis-coded modulation Multilevel coding Shaping Coding for the AWGN channel; block and convolutional codes; lattice and trellis codes; capacity-approaching codes; equalization of linear Gaussian channels; linear, decision-feedback, and MLSD equalization; precoding; multicarrier modulation; and topics in wireless communication. Description from the course home page: 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. 2003-06 Learning Object 6.451-Spring2003 local: 6.451 local: IMSCP-MD5-7b3d320cf570ec10712dab3722572ec0 http://hdl.handle.net/1721.1/36834 en-US Usage Restrictions: This site (c) Massachusetts Institute of Technology 2003. Content within individual courses is (c) by the individual authors unless otherwise noted. The Massachusetts Institute of Technology is providing this Work (as defined below) under the terms of this Creative Commons public license ("CCPL" or "license"). The Work is protected by copyright and/or other applicable law. Any use of the work other than as authorized under this license is prohibited. By exercising any of the rights to the Work provided here, You (as defined below) accept and agree to be bound by the terms of this license. The Licensor, the Massachusetts Institute of Technology, grants You the rights contained here in consideration of Your acceptance of such terms and conditions. text/html Spring 2003 |
| spellingShingle | coding techniques the Shannon limit of additive white Gaussian noise channels Small signal constellations performance analysis coding gain Hard-decision and soft-decision decoding Introduction to binary linear block codes Reed-Muller codes finite fields Reed-Solomon and BCH codes binary linear convolutional codes Viterbi and BCJR algorithms Trellis representations of binary linear block codes trellis-based ML decoding Codes on graphs sum-product max-product decoding algorithms Turbo codes LDPC codes and RA codes Coding for the bandwidth-limited regime Lattice codes Trellis-coded modulation Multilevel coding Shaping Forney, G. David 6.451 Principles of Digital Communication II, Spring 2003 |
| title | 6.451 Principles of Digital Communication II, Spring 2003 |
| title_full | 6.451 Principles of Digital Communication II, Spring 2003 |
| title_fullStr | 6.451 Principles of Digital Communication II, Spring 2003 |
| title_full_unstemmed | 6.451 Principles of Digital Communication II, Spring 2003 |
| title_short | 6.451 Principles of Digital Communication II, Spring 2003 |
| title_sort | 6 451 principles of digital communication ii spring 2003 |
| topic | coding techniques the Shannon limit of additive white Gaussian noise channels Small signal constellations performance analysis coding gain Hard-decision and soft-decision decoding Introduction to binary linear block codes Reed-Muller codes finite fields Reed-Solomon and BCH codes binary linear convolutional codes Viterbi and BCJR algorithms Trellis representations of binary linear block codes trellis-based ML decoding Codes on graphs sum-product max-product decoding algorithms Turbo codes LDPC codes and RA codes Coding for the bandwidth-limited regime Lattice codes Trellis-coded modulation Multilevel coding Shaping |
| url | http://hdl.handle.net/1721.1/36834 |
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