Swift HARQ Based on Machine Learning for Latency Minimization in URLLC

Abstract

Ultra-reliable low-latency communication (URLLC) has been introduced in the 5th Generation (5G) radios for mission-critical applications that demand strict reliability and latency traffic to guarantee the rapid delivery of short packets (up to 1 ms) with a success probability rate of 99.999%. The challenging reliability and latency requirements of URLLC have significant impact on the air-interface design, especially on the Hybrid Automatic Repeat reQuest (HARQ) mechanism. This study focuses on satisfying link latency requirements by reducing the delay that arises in the presence of the HARQ operation. To this end, we propose a Swift HARQ protocol empowered by machine learning techniques to estimate the decodability of a packet early enough within its maximum number of allowable retransmission attempts. This can allow the transmitter to react faster by dropping the non-decodable packets, or activating the repetition mode where parts of the HARQ feedback can be omitted. As shown through system-level simulations, the proposed model achieves a delay reduction of more than 50% compared to the traditional HARQ, and increases the system throughput by up to 40% when multiple HARQ retransmissions are required.