Feedback Prediction for Proactive HARQ in the Context of Industrial Internet of Things

TL;DR

This paper proposes an enhanced proactive HARQ protocol with feedback prediction for IIoT, demonstrating significant energy efficiency improvements over classical methods under various latency and BLER constraints.

Contribution

It introduces a feedback prediction mechanism into proactive HARQ, significantly improving energy efficiency and latency performance in IIoT applications.

Findings

Enhanced protocol outperforms classical proactive HARQ in energy efficiency.

Prediction-based HARQ surpasses reactive HARQ with large feedback delays.

Energy gains of 11-15% at 1 ms latency, 4-7.5% at 2 ms latency.

Abstract

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.