Graph Convolutional Network Enabled Power-Constrained HARQ Strategy for URLLC

TL;DR

This paper proposes a graph convolutional network-based power optimization strategy for HARQ schemes to achieve ultra-reliable low-latency communication with limited power, demonstrating HARQ-IR's superior latency performance.

Contribution

It introduces a GCN-based approach for power allocation in HARQ schemes to optimize latency and reliability under power constraints in URLLC.

Findings

HARQ-IR achieves lowest latency with high reliability.

GCN effectively handles the non-convex optimization problem.

HARQ-IR has higher coding complexity but better performance.

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.