Autonomous systems and mission-critical applications demand ultra-reliable low-latency communication (URLLC). To build wireless communication networks capable of accommodating such applications, optimization of the air-interface characteristics is vital. This paper leverages recent advancements in the field of Artificial Intelligence (AI) technologies to optimize specific aspects of the air interface design to satisfy these stringent link reliability and latency requirements. The precise aim of this research is to reduce the link latency caused by the presence of the Hybrid Automatic Repeat reQuest (HARQ) mechanism. To this end, we propose a novel deep learning-based algorithm (Deep-HARQ), employing a deep neural network (DNN) with fully connected layers to estimate the decodability of the coded-received in-phase and quadrature (I/Q) signals prior to accomplishing the majority of the complex reception tasks. This enables the receiver to respond faster, allowing for the reduction of the signal round-trip time (RTT). To evaluate Deep-HARQ with a realistic dataset, we collected training and validation samples from a waveform compatible with 3GPP 5G NR Release 15 standards. The simulation results reveal a faster estimation response, with an accuracy enhancement of 12% compared to relevant algorithms in the literature.
The fifth-generation wireless communication networks (5G) facilitate a wide range of newly-emerging applications alongside existing cellular mobile broadband services. One of the key service classes of 5G is Ultra-Reliable and Low-Latency Communications (URLLC), which guarantees 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 cannot be delivered by existing cellular networks, resulting in the need for significant air interface modifications. This study aims to satisfy the link latency requirements of URLLC applications, and specifically reduce the latency associated with the presence of the Hybrid Automatic Repeat reQuest (HARQ) feedback scheme. To this end, we investigate a supervised learning method to provide early HARQ (E-HARQ) feedback on the decodability status of the coded-received signal, ahead of the decoding processing. This strategy allows the transmitter to react faster and minimize the signal round-trip time (RTT). The simulation results demonstrate the capability of the proposed mechanism to speed up the feedback releasing and enhance the prediction accuracy by 12% with the introduction of a new feature derived by the channel state estimation.