Communication Dans Un Congrès Année : 2026

Sparse VLSF Codes Optimization for Short-Packet Transmission via Saddlepoint Methods

Résumé

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 a 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.

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hal-05592908 , version 1 (15-04-2026)

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  • HAL Id : hal-05592908 , version 1

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Guodong Sun, Samir M. Perlaza, Philippe Mary, Jean-Marie Gorce. Sparse VLSF Codes Optimization for Short-Packet Transmission via Saddlepoint Methods. IEEE ICC 2026 - IEEE International Conference on Communications, May 2026, Glasgow, United Kingdom. ⟨hal-05592908⟩
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