Low-Complexity Block-Based Decoding Algorithms for Short Block Channels
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| Publicado en: | arXiv.org (Apr 15, 2024), p. n/a |
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| Autor Principal: | |
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| Publicado: |
Cornell University Library, arXiv.org
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| Materias: | |
| Acceso en liña: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3040956430 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 024 | 7 | |a 10.1109/AFRICON55910.2023.10293604 |2 doi | |
| 035 | |a 3040956430 | ||
| 045 | 0 | |b d20240415 | |
| 100 | 1 | |a Mody Sy | |
| 245 | 1 | |a Low-Complexity Block-Based Decoding Algorithms for Short Block Channels | |
| 260 | |b Cornell University Library, arXiv.org |c Apr 15, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a This paper presents low-complexity block-based encoding and decoding algorithms for short block length channels. In terms of the precise use-case, we are primarily concerned with the baseline 3GPP Short block transmissions in which payloads are encoded by Reed-Muller codes and paired with orthogonal DMRS. In contemporary communication systems, the short block decoding often employs the utilization of DMRS-based least squares channel estimation, followed by maximum likelihood decoding. However, this methodology can incur substantial computational complexity when processing long bit length codes. We propose an innovative approach to tackle this challenge by introducing the principle of block/segment encoding using First-Order RM Codes which is amenable to low-cost decoding through block-based fast Hadamard transforms. The Block-based FHT has demonstrated to be cost-efficient with regards to decoding time, as it evolves from quadric to quasi-linear complexity with a manageable decline in performance. Additionally, by incorporating an adaptive DMRS/data power adjustment technique, we can bridge/reduce the performance gap and attain high sensitivity, leading to a good trade-off between performance and complexity to efficiently handle small payloads. | |
| 653 | |a Communications systems | ||
| 653 | |a Algorithms | ||
| 653 | |a Maximum likelihood estimation | ||
| 653 | |a Payloads | ||
| 653 | |a Channels | ||
| 653 | |a Complexity | ||
| 653 | |a Maximum likelihood decoding | ||
| 653 | |a Coding | ||
| 700 | 1 | |a Knopp, Raymond | |
| 773 | 0 | |t arXiv.org |g (Apr 15, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3040956430/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2404.10798 |