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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.12834 |
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| _version_ | 1866917899547967488 |
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| author | Bacinoglu, B. Tan |
| author_facet | Bacinoglu, B. Tan |
| contents | In this paper, we introduce an achievability bound on the frame error rate of random tree code ensembles under a sequential decoding algorithm with a hard computational limit and consider the optimization of the random tree code ensembles over their branching structures/profiles and the decoding measure. Through numerical examples, we show that the achievability bound for the optimizated random tree codes can approach the maximum likelihood (ML) decoding performance of pure random codes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12834 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Optimization of Random Tree Codes for Limited Computational Resources Bacinoglu, B. Tan Information Theory Computational Complexity In this paper, we introduce an achievability bound on the frame error rate of random tree code ensembles under a sequential decoding algorithm with a hard computational limit and consider the optimization of the random tree code ensembles over their branching structures/profiles and the decoding measure. Through numerical examples, we show that the achievability bound for the optimizated random tree codes can approach the maximum likelihood (ML) decoding performance of pure random codes. |
| title | The Optimization of Random Tree Codes for Limited Computational Resources |
| topic | Information Theory Computational Complexity |
| url | https://arxiv.org/abs/2501.12834 |