云无线接入网中用于早期HARQ反馈预测的分布式机器学习
Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs
AI总结：
本文提出了一种用于Cloud RANs的分布式机器学习HARQ预测方案DA2SGMM，通过速率受限的反馈信道在UE端提前预测BBU解码结果，仿真表明其在吞吐量和延迟上显著优于常规HARQ等方案。
摘要：
在本研究中，我们为Cloud RANs（C-RANs）提出了一种新颖的HARQ预测方案，该方案利用来自Remote Radio Heads（RRHs）的速率受限反馈信道（2-6比特）的反馈，在User Equipment（UE）处提前于实际解码预测BaseBand Unit（BBU）的解码结果。具体而言，我们提出了一种Dual Autoencoding 2-Stage Gaussian Mixture Model（DA2SGMM），该模型在整个C-RAN设置中以端到端的方式进行训练。使用100 GHz的sub-THz频段的现实链路级仿真，我们表明新颖的DA2SGMM HARQ预测方案明显优于所有其他改编的和最先进的方案。DA2SGMM在遮挡检测以及无遮挡和单遮挡情况下的HARQ预测方面表现出卓越的性能。具体而言，具有4比特反馈的DA2SGMM与其次优替代方案相比，平均吞吐量提高了200%以上。与常规HARQ相比，DA2SGMM将最大传输延迟降低了72.4%以上，同时在无遮挡场景中保持了75%以上的吞吐量。在单遮挡场景中，与常规HARQ相比，DA2SGMM在大多数评估的信噪比（SNRs）下显著提高了吞吐量。
英文摘要：
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.
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