Abstract
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.