论文标题
分布式机器学习，用于早期HARQ反馈预测
Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs
论文作者
论文摘要
在这项工作中，我们提出了针对云跑（C-RAN）的新型HARQ预测方案（C-RAN），这些方案在远程无线电头（RRHS）的速率限制反馈通道（2-6位）上使用反馈，以在用户设备（UE）预测基数单元（BBU）的解码结果（UE）。特别是，我们提出了一个双自动编码的2阶段高斯混合模型（DA2SGMM），该模型在整个C-RAN设置中以端到端的方式进行了训练。使用100 GHz的Sub-Thz频段中的逼真的链路级模拟，我们表明，新颖的DA2SGMM HARQ预测方案显然优于所有其他适应和最先进的方案。 DA2SGMM在阻塞检测以及在不障碍物和单个障碍物中的HARQ预测方面表现出了出色的性能。特别是，与最佳替代品相比，具有4个位反馈的DA2SGMM平均吞吐量高200％以上。与常规HARQ相比，DA2SGMM将最大传输潜伏期降低了72.4％以上，同时在不块状情况下保持了超过75％的吞吐量。与常规HARQ相比，在单块场景中，DA2SGMM显着增加了大多数评估的信噪比（SNR）的吞吐量。
In this work, we propose novel HARQ prediction schemes for Cloud RANs (C-RANs) that use feedback over a rate-limited feedback channel (2 - 6 bits) from the Remote Radio Heads (RRHs) to predict at the User Equipment (UE) the decoding outcome at the BaseBand Unit (BBU) ahead of actual decoding. In particular, we propose a Dual Autoencoding 2-Stage Gaussian Mixture Model (DA2SGMM) that is trained in an end-to-end fashion over the whole C-RAN setup. Using realistic link-level simulations in the sub-THz band at 100 GHz, we show that the novel DA2SGMM HARQ prediction scheme clearly outperforms all other adapted and state-of-the-art schemes. The DA2SGMM shows a superior performance in terms of blockage detection as well as HARQ prediction in the no-blockage and single-blockage cases. In particular, the DA2SGMM with 4~bit feedback achieves a more than 200 % higher throughput in average compared to its best alternative. Compared to regular HARQ, the DA2SGMM reduces the maximum transmission latency by more than 72.4 %, while maintaining more than 75 % of the throughput in the no-blockage scenario. In the single-blockage scenario, DA2SGMM significantly increases the throughput for most of the evaluated Signal-to-Noise-Ratios (SNRs) compared to regular HARQ.
