In this paper, a power-constrained hybrid automatic repeat request (HARQ) transmission strategy is developed to support ultra-reliable low-latency communications (URLLC). In particular, we aim to minimize the delivery latency of HARQ schemes over time-correlated fading channels, meanwhile ensuring the high reliability and limited power consumption. To ease the optimization, the simple asymptotic outage expressions of HARQ schemes are adopted. Furthermore, by noticing the non-convexity of the latency minimization problem and the intricate connection between different HARQ rounds, the graph convolutional network (GCN) is invoked for the optimal power solution owing to its powerful ability of handling the graph data. The primal-dual learning method is then leveraged to train the GCN weights. Consequently, the numerical results are presented for verification together with the comparisons among three HARQ schemes in terms of the latency and the reliability, where the three HARQ schemes include Type-I HARQ, HARQ with chase combining (HARQ-CC), and HARQ with incremental redundancy (HARQ-IR). To recapitulate, it is revealed that HARQ-IR offers the lowest latency while guaranteeing the demanded reliability target under a stringent power constraint, albeit at the price of high coding complexity.

翻译：本文提出了一种功率受限的混合自动重传请求（HARQ）传输策略，以支持超可靠低延迟通信（URLLC）。具体而言，我们旨在最小化HARQ方案在时间相关衰落信道上的传输延迟，同时确保高可靠性和有限功耗。为简化优化过程，采用了HARQ方案的简单渐近中断表达式。此外，考虑到延迟最小化问题的非凸性以及不同HARQ轮次之间的复杂关联，利用图卷积网络（GCN）对图数据的强大处理能力，求解最优功率分配方案。随后采用原始-对偶学习方法训练GCN权重。最后，通过数值结果进行验证，并比较了三种HARQ方案（包括Type-I HARQ、带追逐合并的HARQ（HARQ-CC）以及带增量冗余的HARQ（HARQ-IR））在延迟和可靠性方面的性能。总结而言，研究表明，在严格功率约束下，HARQ-IR能够在保证所需可靠性目标的同时提供最低延迟，但代价是较高的编码复杂度。