标题： 不要干扰而合作：一种完全可学习的多址信道反馈代码设计 显示英文标题

标题： Do not Interfere but Cooperate: A Fully Learnable Code Design for Multi-Access Channels with Feedback

摘要： 基于数据驱动的深度学习编码设计，包括针对现有码的低复杂度神经解码器或端到端可训练的自动编码器，已经展现出令人印象深刻的结果，特别是在我们没有高性能的结构化码设计方案的场景中。 然而，大多数现有的基于数据驱动的信道编码解决方案都集中在点对点的场景上。 在这项工作中，我们考虑了一个具有反馈的多址接入信道（MAC），并试图了解基于深度学习的设计是否能够实现编码器之间的协调与合作，同时允许错误纠正。 仿真结果显示，所提出的多址块注意反馈（MBAF）码在有限块长的情况下提高了无反馈MAC的可达速率上限。 显示英文摘要

摘要： 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.