BBAuthors: Strodthoff, N.; Advisor: -; Participants: Göktepe,B.; Schierl, T.; Hellge, C.; Samek, W. (2019)
We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine
learning techniques as a path towards ultra-reliable and lowlatency communication (URLLC). To this end, we propose machine learning methods to predict the outcome of the decoding process ahead of the end of the transmission. We discuss different input features and classification algorithms ranging from traditional methods to newly developed supervised autoencoders. These methods are evaluated based on their prospects of complying with the URLLC requirements of effective block error rates below 10 at small latency overheads. We provide
realistic performance estimates in a system model incorporating scheduling effects to demonstrate the feasibility of E-HARQ across different signal-to-n... 
