Isolated training with Gaussian priors (TGP) of the component autoencoders of turbo-autoencoder architectures enables faster, more consistent training and better generalization to arbitrary decoding iterations than training based on deep unfolding. We propose fitting the components via extrinsic information transfer (EXIT) charts to a desired behavior which enables scaling to larger message lengths ($k \approx 1000$) while retaining competitive performance. To the best of our knowledge, this is the first autoencoder that performs close to classical codes in this regime. Although the binary cross-entropy (BCE) loss function optimizes the bit error rate (BER) of the components, the design via EXIT charts enables to focus on the block error rate (BLER). In serially concatenated systems the component-wise TGP approach is well known for inner components with a fixed outer binary interface, e.g., a learned inner code or equalizer, with an outer binary error correcting code. In this paper we extend the component training to structures with an inner and outer autoencoder, where we propose a new 1-bit quantization strategy for the encoder outputs based on the underlying communication problem. Finally, we discuss the model complexity of the learned components during design time (training) and inference and show that the number of weights in the encoder can be reduced by 99.96 %.

翻译：在高斯先验下对turbo自编码器架构的分量自编码器进行孤立训练（TGP），相较于基于深度展开的训练方法，能够实现更快速、更一致的训练，并提升对任意解码迭代的泛化能力。我们提出通过外信息转移图拟合分量至期望行为，从而支持扩展至更长消息长度（k≈1000），同时保持竞争性性能。据我们所知，这是首个在该场景下性能接近经典编码的自编码器。尽管二元交叉熵损失函数优化了分量的误比特率，但通过外信息转移图的设计能够聚焦于误块率。在串行级联系统中，对于具有固定外部二元接口（例如内部编码或均衡器与外部纠错码级联）的内分量，分量级TGP方法已广为人知。本文将该分量训练扩展至具有内、外自编码器的结构，并提出一种基于通信问题的新型1比特量化策略用于编码器输出。最后，我们讨论了学习分量在设计阶段（训练）和推理时的模型复杂度，并表明编码器的权重数量可减少99.96%。