Noise-Centric Decoding
Maximum Likelihood (ML) decoding of forward error correction codes is known to be optimally accurate, but is not used in practice due to the lack of a feasible implementation. As the common approach in coding theory is a code-centric one, designing a ML decoder is a challenging code-specific task. W...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/140190 |
| _version_ | 1826204443010400256 |
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| author | Solomon, Amit |
| author2 | Médard, Muriel |
| author_facet | Médard, Muriel Solomon, Amit |
| author_sort | Solomon, Amit |
| collection | MIT |
| description | Maximum Likelihood (ML) decoding of forward error correction codes is known to be optimally accurate, but is not used in practice due to the lack of a feasible implementation. As the common approach in coding theory is a code-centric one, designing a ML decoder is a challenging code-specific task. We establish a noise-centric approach for decoding of error correction codes that enables us to introduce a universal ML soft detection decoder called Soft Guessing Random Additive Noise Decoder (SGRAND), which is a development of a previously described hard detection ML decoder called Guessing Random Additive Noise Decoder (GRAND), that fully avails of soft detection information. SGRAND is suitable for use with any arbitrary moderate redundancy block code. A further development of the algorithm is provided that can decode coded signals transmitted on Multiple Access Channels (MACs), where transmitters not only suffer from noise, but also interfere one another. We propose a scheme that deals with the two problems of MAC separately: interference and the noise. We prove that a scheme based on SGRAND results in optimally accurate decodings. Finally, we study how correlated noise between orthogonal channels can be used to improve rates and reduce Block Error Rate (BLER) performance via a scheme called Noise Recycling. |
| first_indexed | 2024-09-23T12:55:08Z |
| format | Thesis |
| id | mit-1721.1/140190 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:55:08Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1401902022-02-08T03:33:58Z Noise-Centric Decoding Solomon, Amit Médard, Muriel Duffy, Ken R. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Maximum Likelihood (ML) decoding of forward error correction codes is known to be optimally accurate, but is not used in practice due to the lack of a feasible implementation. As the common approach in coding theory is a code-centric one, designing a ML decoder is a challenging code-specific task. We establish a noise-centric approach for decoding of error correction codes that enables us to introduce a universal ML soft detection decoder called Soft Guessing Random Additive Noise Decoder (SGRAND), which is a development of a previously described hard detection ML decoder called Guessing Random Additive Noise Decoder (GRAND), that fully avails of soft detection information. SGRAND is suitable for use with any arbitrary moderate redundancy block code. A further development of the algorithm is provided that can decode coded signals transmitted on Multiple Access Channels (MACs), where transmitters not only suffer from noise, but also interfere one another. We propose a scheme that deals with the two problems of MAC separately: interference and the noise. We prove that a scheme based on SGRAND results in optimally accurate decodings. Finally, we study how correlated noise between orthogonal channels can be used to improve rates and reduce Block Error Rate (BLER) performance via a scheme called Noise Recycling. Ph.D. 2022-02-07T15:29:25Z 2022-02-07T15:29:25Z 2021-09 2021-09-21T19:30:51.267Z Thesis https://hdl.handle.net/1721.1/140190 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Solomon, Amit Noise-Centric Decoding |
| title | Noise-Centric Decoding |
| title_full | Noise-Centric Decoding |
| title_fullStr | Noise-Centric Decoding |
| title_full_unstemmed | Noise-Centric Decoding |
| title_short | Noise-Centric Decoding |
| title_sort | noise centric decoding |
| url | https://hdl.handle.net/1721.1/140190 |
| work_keys_str_mv | AT solomonamit noisecentricdecoding |