In this work, we introduce a framework that enables the use of Syndrome-Based Neural Decoders (SBND) for high-order Bit-Interleaved Coded Modulations (BICM). To this end, we extend the previous results on SBND, for which the validity is limited to Binary Phase-Shift Keying (BPSK), by means of a theoretical channel modeling of the bit Log-Likelihood Ratio (bit-LLR) induced outputs. We implement the proposed SBND system for two polar codes $(64,32)$ and $(128,64)$, using a Recurrent Neural Network (RNN) and a Transformer-based architecture. Both implementations are compared in Bit Error Rate (BER) performance and computational complexity.

翻译：本文提出了一种框架，使得基于综合征的神经译码器（SBND）能够用于高阶比特交织编码调制（BICM）。为此，我们通过理论信道建模比特对数似然比（bit-LLR）诱导输出，扩展了先前仅适用于二进制相移键控（BPSK）的SBND结果。我们采用循环神经网络（RNN）和基于Transformer的架构，针对两种极化码$(64,32)$和$(128,64)$实现了所提出的SBND系统。两种实现方式在误码率（BER）性能和计算复杂度方面进行了比较。