Relaying-Enabled Ultra-Reliable Low-Latency Communications in 5G

TLDR

Supporting ultra‑reliable low‑latency communications (URLLC) in 5G relies on cooperative relaying, yet prior work assumes ideal Shannon capacity, which does not reflect the strict timing and short‑blocklength coding required in real URLLC deployments. This article models and optimizes relaying‑enabled URLLC networks. We present accurate performance models for relay‑enabled 5G, compare relaying to direct transmission under noise‑limited and interference‑limited scenarios, and provide optimization tools based on perfect or average channel state information. The study summarizes the resulting optimization schemes and outlines future research directions.

Abstract

Supporting URLLC has become one of the major considerations in the design of 5G systems. In the literature, it has been shown that cooperative relaying is an efficient strategy to improve the reliability of transmissions, support higher rates, and lower latency. However, prior studies have demonstrated the performance advantages of relaying generally under the ideal assumption of communicating arbitrarily reliably at Shannon's channel capacity, which is not an accurate performance indicator for relaying in URLLC networks in which transmission is required to be completed within a strict time span and coding schemes with relatively short blocklengths need to be employed. In this article, we address the performance modeling and optimization of relaying-enabled URLLC networks. We first discuss the accurate performance modeling of relay-enabled 5G networks. In particular, we provide a comprehensive summary of the performance advantage of applying relaying in 5G URLLC transmissions in comparison to the case of direct transmission (without relaying). Both a noise-limited scenario and an interference- limited scenario are discussed. Then we present tools for performance optimization utilizing the knowledge of either perfect or average channel side information. Finally, we summarize the proposed optimization schemes and discuss potential future research directions.