Optimization of Sparse VLSF Codes for Short-Packet Transmission via Saddlepoint Methods
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910139575959552 |
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| author | Sun, Guodong Perlaza, Samir M. Mary, Philippe Gorce, Jean-Marie |
| author_facet | Sun, Guodong Perlaza, Samir M. Mary, Philippe Gorce, Jean-Marie |
| contents | In this work, we present an optimization framework for sparse variable-length stop-feedback (VLSF) codes based on a saddlepoint approximation, which jointly optimizes the decoding configuration parameters. Thanks to the analytical tractability of the saddlepoint approximation, the framework enables efficient gradient-based optimization of such parameters for common memoryless channels, including the additive white Gaussian noise, binary symmetric, and binary erasure channels. We further propose a refined decoding rule that extends the conventional fixed-threshold rule and leads to a tighter achievability bound. Numerical results demonstrate that our framework provides near-optimal decoding configurations at low computational cost. Moreover, the results from our refined rule demonstrate that the fixed-threshold decoding rule is restrictive and that achievability bounds can be further tightened. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16049 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Optimization of Sparse VLSF Codes for Short-Packet Transmission via Saddlepoint Methods Sun, Guodong Perlaza, Samir M. Mary, Philippe Gorce, Jean-Marie Information Theory In this work, we present an optimization framework for sparse variable-length stop-feedback (VLSF) codes based on a saddlepoint approximation, which jointly optimizes the decoding configuration parameters. Thanks to the analytical tractability of the saddlepoint approximation, the framework enables efficient gradient-based optimization of such parameters for common memoryless channels, including the additive white Gaussian noise, binary symmetric, and binary erasure channels. We further propose a refined decoding rule that extends the conventional fixed-threshold rule and leads to a tighter achievability bound. Numerical results demonstrate that our framework provides near-optimal decoding configurations at low computational cost. Moreover, the results from our refined rule demonstrate that the fixed-threshold decoding rule is restrictive and that achievability bounds can be further tightened. |
| title | Optimization of Sparse VLSF Codes for Short-Packet Transmission via Saddlepoint Methods |
| topic | Information Theory |
| url | https://arxiv.org/abs/2604.16049 |