  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.
