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Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G. (arXiv:1807.10495v2 [cs.IT] UPDATED)

(Submitted on 27 Jul 2018 (v1), last revised 25 Oct 2019 (this version, v2))

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.

Submission history

From: Nils Strodthoff [view email]

[v1]
Fri, 27 Jul 2018 08:51:02 UTC (458 KB)

[v2]
Fri, 25 Oct 2019 08:14:04 UTC (1,864 KB)

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