Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G

TL;DR

This paper explores machine learning-enhanced early HARQ feedback schemes to improve ultra-reliable low-latency communication in 5G, demonstrating their feasibility and benefits over traditional methods.

Contribution

It introduces novel machine learning methods, including supervised autoencoders, for predicting HARQ outcomes before transmission ends, advancing URLLC performance.

Findings

Achieves block error rates below 10^-5 with low latency overheads

Demonstrates improved prediction accuracy over traditional HARQ schemes

Shows robustness across various system conditions

Abstract

We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine learning techniques as a path towards ultra-reliable and low-latency 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 $10^{-5}$ 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-noise ratios, subcode lengths, channel conditions and system loads, and show the benefit over regular HARQ and existing E-HARQ schemes without machine learning.