A new method for erasure decoding of convolutional codes
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910916520443904 |
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| author | Lieb, Julia Pinto, Raquel Vela, Carlos |
| author_facet | Lieb, Julia Pinto, Raquel Vela, Carlos |
| contents | In this paper, we propose a new erasure decoding algorithm for convolutional codes using the generator matrix. This implies that our decoding method also applies to catastrophic convolutional codes in opposite to the classic approach using the parity-check matrix. We compare the performance of both decoding algorithms. Moreover, we enlarge the family of optimal convolutional codes (complete-MDP) based on the generator matrix. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15873 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A new method for erasure decoding of convolutional codes Lieb, Julia Pinto, Raquel Vela, Carlos Information Theory In this paper, we propose a new erasure decoding algorithm for convolutional codes using the generator matrix. This implies that our decoding method also applies to catastrophic convolutional codes in opposite to the classic approach using the parity-check matrix. We compare the performance of both decoding algorithms. Moreover, we enlarge the family of optimal convolutional codes (complete-MDP) based on the generator matrix. |
| title | A new method for erasure decoding of convolutional codes |
| topic | Information Theory |
| url | https://arxiv.org/abs/2504.15873 |