Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction
  in 5G   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.

22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc