Optimization of Sparse VLSF Codes for Short-Packet Transmission via Saddlepoint Methods
This work provides a practical optimization method for short-packet transmission codes, which is important for low-latency communication systems.
The paper presents an optimization framework for sparse variable-length stop-feedback codes using saddlepoint approximation, enabling efficient gradient-based optimization of decoding parameters. Numerical results show near-optimal decoding configurations at low computational cost.
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.