论文标题
在工业互联网的背景下，主动哈q的反馈预测
Feedback Prediction for Proactive HARQ in the Context of Industrial Internet of Things
论文作者
论文摘要
在这项工作中，我们使用链接级模拟在工业互联网（IIOT）应用程序的背景下，使用链接级仿真（HARQ）进行了主动混合自动重复请求（HARQ）（HARQ）。特别是，我们使用反馈预测机制提出了增强的主动HARQ协议。我们表明，增强的协议在能源效率方面对几乎所有评估的BLER目标至少对于足够大的反馈延迟而言，几乎所有经过评估的BLER目标都取得了显着的增长。此外，我们证明，在所有情况下，在所有情况下，在所有情况下，由于较不复杂的预测方法，在所有情况下，提出的协议在所有情况下都显然超过了经典的主动HARQ，从而在$ 10^{ - 2} $ 10^{-2} $ bler for 4％的范围内提高了15％的能效增长，范围为15％，范围为15％。 $ 10^{ - 3} $ bler。此外，我们表明，具有预测的功率约束的主动性Harq甚至超过了不受限制的反应性HARQ，对于足够大的反馈延迟。
In this work, we investigate proactive Hybrid Automatic Repeat reQuest (HARQ) using link-level simulations for multiple packet sizes, modulation orders, BLock Error Rate (BLER) targets and two delay budgets of 1 ms and 2 ms, in the context of Industrial Internet of Things (IIOT) applications. In particular, we propose an enhanced proactive HARQ protocol using a feedback prediction mechanism. We show that the enhanced protocol achieves a significant gain over the classical proactive HARQ in terms of energy efficiency for almost all evaluated BLER targets at least for sufficiently large feedback delays. Furthermore, we demonstrate that the proposed protocol clearly outperforms the classical proactive HARQ in all scenarios when taking a processing delay reduction due to the less complex prediction approach into account, achieving an energy efficiency gain in the range of 11% up to 15% for very stringent latency budgets of 1 ms at $10^{-2}$ BLER and from 4% up to 7.5% for less stringent latency budgets of 2 ms at $10^{-3}$ BLER. Furthermore, we show that power-constrained proactive HARQ with prediction even outperforms unconstrained reactive HARQ for sufficiently large feedback delays.
