Data-driven deep learning based code designs, including low-complexity neural decoders for existing codes, or end-to-end trainable auto-encoders have exhibited impressive results, particularly in scenarios for which we do not have high-performing structured code designs. However, the vast majority of existing data-driven solutions for channel coding focus on a point-to-point scenario. In this work, we consider a multiple access channel (MAC) with feedback and try to understand whether deep learning-based designs are capable of enabling coordination and cooperation among the encoders as well as allowing error correction. Simulation results show that the proposed multi-access block attention feedback (MBAF) code improves the upper bound of the achievable rate of MAC without feedback in finite block length regime.

翻译：数据驱动的深度学习码本设计，包括针对现有码字的低复杂度神经解码器或端到端可训练自编码器，已在缺乏高性能结构化码本设计的场景中展现出显著效果。然而，现有基于数据驱动的信道编码解决方案绝大多数聚焦于点对点场景。本研究考虑带反馈的多接入信道（MAC），旨在探究基于深度学习的设计能否实现编码器间的协调与协作及错误校正。仿真结果表明，所提出的多接入块注意力反馈（MBAF）码在有限块长条件下，能够提升无反馈MAC可达速率的上界。